Motivation

The internet is an important source of information for many people, and today’s social media platforms continue to shape how people access and act on health information.

Social media platforms serve as channels for top-down communication from health officials to the public, for peer sharing of health information across users, and for finding communities with shared health goals and challenges.

Nonetheless, along with these useful interactions come false information that has the potential to cause people to take harmful measures, reject factual updates from authorities, and upend the work of local health institutions.

Events such as the 2014 Ebola epidemic and today’s COVID-19 pandemic bring to light the need for social media platforms to facilitate access to accurate and reliable information.

In this project, we aim to use a mixed-method approach to study the use of social media as an information channel during the ongoing COVID-19 pandemic in Nigeria – what accounts shared news that was later found to be false, how did false news spread within the network before they were corrected, why do people share certain kinds of information, and what strategies can we learn to navigate the spread of health misinformation online within developing and under-developing countries.

Outcomes

The overall objective of this project is to study the dynamics of the spread of factual and false information in online social networks in Nigeria during a pandemic.

Our study will combine approaches in social network feature engineering and analysis, machine learning (ML), and natural language processing (NLP) with qualitative insights from social network users.

These findings will help online platforms, journalists, the general public, and health institutions in Nigeria identify ways that health misinformation is spread online and rethink what strategies can be employed to mitigate the danger it poses.

Our results will include code (in Python and/or R), social media data analyses, anonymized survey data, visualizations, a blog post, and a research publication.

We will release new code or point to existing open-source resources that will be used for our analyses. These will be hosted on Github to allow independent reruns. We will refer to the privacy policies of specific online communities regarding sharing identifiable data. We will release blog posts and visualizations with simple readable information for a wider audience.

We also aim to publish our findings at conference venues interested in the interaction between technology and society, and how both factors influence themselves e.g. CHI Human Factors in Computing Systems), CSCW (Computer Supported Cooperative Work), The Web Conference, WSDM (Web Search and Data Mining), etc.

Long term vision

We hope that this research will support on-the-ground healthcare work by helping to inform how workers interact with the public, and how to address the publics’ constantly changing perception of what is true or not.

We hope to contribute to the joint effort of journalists and government officials to stop the spread of the virus in Nigeria and other developing countries. Our approach will be useful for studying other forms of misinformation in future health crises and/or political events (i.e. elections).

User perceptions of these events are very much shaped by social media, however, is currently understudied in many African countries. Additionally, research on social media echo chambers and political polarization are widely studied in America but not in the African context.

Previous research in this space is focused on diseases like ebola1 but had not specifically focused on Africa2, or HIV but did not include a qualitative analysis3.

Description

Artificial intelligent system for predictors of early detection of maternal, neonatal and child health risks and their timely management.

Rationale

The idea we propose is to build an artificial intelligent (AI) system for informing on predictors of early detection of maternal, neonatal and child health risks and their timely management. Tanzania is among the countries with the highest maternal mortality rates (MMR) in the world. The estimated MMR according to the 2015-2016 Tanzania Demographics and Health Survey (DHS) was 556 per 100,000.

In fact, according to the Partnership for Maternal, Newborn and Child Health (PMNCH), the maternal mortality in Tanzania has changes only slightly over the years contrary to child mortality rates which were 99 deaths per 1000 in 1999 and had become 68 per 1000 by 2005. Therefore, much needs to be done in preventing maternal mortality.

The Ministry of Health Community Development Gender  Elderly and Children (MoHCDGEC) has the District Health Information System (DHIS-2) which digitizes health data at the district level, as well as the Integrated Diseases Surveillance and Response (IDSR) for capturing weekly data on key conditions and diseases. In addition, there is the National Bureau of Statistics (NBS), which conducts and supplies data from the Demographic and Health Survey (DHS). In this proposal we intend to make use of this data present in local, national and international databases and artificial intelligence (AI) tools to build decision-making support systems.

This will involve training AI system i.e. using machine learning algorithm to be able to identify predictors of early risk detection and early risk management. Prior research in integrating AI in health care system in detecting MMR have demonstrated that AI can bring paradigm shift in reducing MMR by predicting the pregnancy outcome.

In the Tanzanian context, the most important determinant of MMR is the timing of detection of risks and their timely management. Full understanding of this aspect will be vital to the fight to reduce MMR as well as neonatal and child deaths. Therefore, our central hypothesis is that computer-based decision procedures, under the broad umbrella of artificial intelligence (AI), can assist in reducing MMR and generally improving health care in poor resources environment through detection of predictors of early risk detection and management.

The uniqueness of our hypothesis is that it will address the crux of the maternal, neonate and child deaths problem, which is what causes untimely detection and management of risks? Understanding of the predictors will help in redesigning the health care practice, management and financing around this area. The decision support tools from this proposal will be applicable in a wider scale, from members of the households, to clinicians, researchers, policymakers and maternal, neonate and child health activists.

Outcomes

The project will involve developing two AI systems (1) An AI system to be used at National level in Tanzania and (2) AI system to be used at hospital level to predict individual cases. However, for this phase (Phase one) we will focus more in the first objective of developing AI system at National level.

Therefore, gathering of data will be divided in two phases. This phase (Phase one) will involve three National platform: District Health Information System (DHIS), Integrated Diseases Surveillance and Response (IDSR)and Demographic and Health Survey (DHS) which is under National Bureau of Statistics (NBS).

DHIS and IDSR were developed in silos and so they do not communicate, they have different sets of indicators. DHIS is under the custodian of the Ministry of Health Community Development Gender, Elderly and Children (MoHCDGEC) and is an electronic tool for digitizing data at the district level. While IDSR is used for capturing weekly data on key conditions and diseases.

Secondary data concerning maternal and neonatal and child risks filtered and cleaned from DHIS, IDSR and NBC will be generated. These data will be used to generate spectrum of factor and their weights for determining the timing of risk detection and their management at national level.

Moreover, in phase two of this project individual routine data to be collected from health facilities will be used to extract factors associated with MMR threat, in order to determine the likelihood of MMR maternal, neonatal and child health risks before and after the pregnancy. This will assist the hospital management to act and intervene at individual level.

Long-term vision

Once the models have been selected they will continue being tested with more incoming data. The second phase of this project will involve development of AI system of early detection of maternal, neonatal and child health risks at hospital level, which will also be integrated to the developed predictions models and data sources at national level.

If this pilot study will show positive results. A future project will involve testing and scale up the developed tool to be used as a control intervention schemes in other areas and even to scale up for other diseases.

Personnel

Dr. Gladness G. Mwanga holds a PhD in Information and Communication Science and Engineering focusing on decision-support tools using Machine learning. She is a mentor in Data science at NOTTECH Lab. In the past four years she has been working on a data science projects that gave her experience in building AI systems in solving various problems within the society. One of them she managed to develop machine learning models to predict decisions to be made by dairy farmers, identifying factors that can influence their decisions and forecast on farmers demands regarding to their specific needs or services in four Eastern Africa countries (Ethiopia, Kenya, Tanzania and Uganda). Gladness also has four years’ experience of working as a research assistant at The Nelson Mandela African Institution of Science and Technology and a consultant in developing ICT based platforms, visualization tools and oversee all activities to ensure a successful project. She’s is going to lead this project and assist in the development of an AI system.

Mr. Timothy Wikedzi is a Senior Software Engineer and a mentor at Nottech Lab, he has intensive experience in building and managing large scale software solutions. Since 2018 he has been a part of the Core team that built and support services for ShowClix Inc an Event Management and Ticketing company based in Pittsburgh USA. Prior to that Mr. Timothy has worked as Lead Tech Consultant in projects that helped to build tools and services for various Organizations in Tanzania, UK, and the USA. Timothy’s areas of interest are in building scalable solution, secured web applications, building fast and efficient systems, forming and leading teams behind software products. He  therefore brings in skills in system development.

Mr. Scott Businge is a Senior Software Engineer and a python mentor at NOTTECH lab, but He has specialized in DevOps and Software engineering with Python and Golang. He has diverse practical
experience and abilities both in software development and Operations. He is also, committed to automation, systems optimisation, security, immediate software delivery practices and monitoring processes. He has previously worked with big tech companies in Africa such Andela which offers world class Software Engineering solutions to clients around the world. He therefore brings in skills in system development using advanced python and launching of the system (DevOps).

Description

The goal of this project is to develop a computer-vision based non-intrusive automatic data collection mechanism to collect images and give insights about ecological succession on coral reefs in the Vamizi Island, allowing biologists to analyze data in real-time and infer on animals life story, behavior and population in Mozambican waters.

Rationale

Coral reefs are among the world’s most diverse ecosystems, with more than 800 species of corals providing habitat and shelter for approximately 25% of global marine life, although they cover less than 0.1% of the ocean floor. Coral reefs are also extremely valuable ecosystems providing livelihood for 1 billion people, and generate 2.7 trillions US Dollars from fisheries, coastal protection, tourism and recreation each year worldwide.

Nevertheless, coral reefs are rapidly declining due to various global and local factors such as overfishing, climate change, ocean acidification, pollution and unsustainable coastal development.

In this context, technological resources have been used for monitoring and analysing the state of coral reefs, and to allow biologists to obtain data in real-time to know about animals’ life story, behaviour, population, and survivorship, collecting valuable data that informs sound decision-making and management/conservation efforts.

Different studies show various approaches for collecting data for marine biodiversity conservation purposes, such as using Remotely Operated Vehicles, Autonomous Underwater Vehicles, and fixed underwater video cameras equipped with Video Analytics Services Platforms.

Most of these studies developed deep learning tools for rapid and large-scale automatic collection and annotation of marine data. However, these studies suggested that to improve current solutions, convolutional neural networks have to be optimised and backup power supplies must be improved.

Moreover, some studies also consider applying infrared cameras, which would enable night-time video capture to create a complete picture of the coral ecosystem. In Africa, however, little or no research has focused on these approaches to apply advanced technology to research marine ecology conservation.

Outcomes

In the long-term, resolving this question will help gain insight on the ecological processes around artificial reefs (particularly important in the context of the oil and gas developments occurring in Mozambique and which will warrant the implementation of reef restoration measures).

Further, this system will be helpful to develop many other research projects which require long periods of observation in remote reefs where permanent and nighttime access is limited. Additionally, this project will create capacity in the young mozambican research community regarding the application of Artificial Intelligence technologies to tackle marine conservation issues.

Vision

This project is an opportunity to pioneer the development of new technologies that will ultimately support conservation effort through enhanced data collection and processing.

The vision is to improve data collection capacity by building on top of already existing systems, namely by developing a different mechanism to provide power supply capable of maintaining such systems in coral reefs located more than a few kilometres from shore by using floating solar panels instead.

In the long-run, the project will be replicated for different coral reefs to allow biologists to obtain data in real-time and learn about animals’ life story, behaviour, and population dynamics. In addition, multiple units would be deployed at several locations to allow for more comprehensive research or monitoring reefs from various angles.

Personnel

Erwan Sola, PhD ( Project Lead), Investigator in the Marine Ecology Department, Faculty of Natural Science, Lúrio University, Mozambique. Experience in project coordination. Coral biology specialist. Extensive fieldwork experience on coral reefs. He will contribute to concept development, project coordination and ecological data analysis.

Luís Pina, MSc Computer Engineering Department, Faculty of  Engineering, Lúrio University, Mozambique. Luis Pina has his Master degree in Information Technology, with experience in developing classification models. He will contribute to this project through data pre-processing and developing classification models. Also, he will be involved in developing the object detection model.

Tiago Azevedo, PhD Candidate Department of Computer Science and Technology, University of Cambridge, United Kingdom. 4th-year Computer Science PhD student, with experience in developing Deep Learning and Machine Learning models in real-world settings. He will contribute to this project through support in coding the object detection model.

Lourenço Matandire , BSc Mechanical Engineering Department, Faculty of Engineering, Lúrio University, Mozambique. Lourenco Matandire is a Mechatronics Engineering that will be responsible for creating and providing the assessment to the Flexible Underwater Observatory (FUO) and managing its power supply.

Boaventura Manhique, BSc Computer Engineering Department, Faculty of Engineering, Lúrio University, Mozambique. Bonaventure Manhique is a Computer Engineering in Networking with a deep understanding of electronics. He will be responsible for maintaining and managing all means of communication and information sharing between the FUO and the biologists.

What is the overall scope of the project?

The project will deliver three main components from research in natural language processing, dataset creation, and policy creation:

1. Fellowship for African AI researchers focused on African languages, based on previously funded work on language datasets. This work contributes to a roadmap for better integration of African languages on digital platforms in aid of lowering the barrier for African participation in the digital economy,
2. Improvement of the representation of AI research carried out on African languages by creating resources for a variety of NLP tasks and in a variety of African languages that will enable good, data-driven results in AI research,
3. Attract an African community of native speakers as contributors of language resources and language technology tools to adopt and support Masakhane NLP, a platform for sharing, maintaining and making use of language resources and tools; establishing widely agreed benchmarks for NLP tasks and stimulating competition between methods and systems,
4. Be used as a model case to inform African evidence-based policymaking concerning Artificial Intelligence and will be included in UNESCO’s AI Decision maker’s Essential to inform policymakers.

This project is lead by 29 researchers, covers 9 African languages, spoken across 22 countries, reaching 300 million speakers

What are the results at the moment?

Result 1: African Language Datasets

Below, we detail the languages of datasets created. Delivering the project Cracking the Language Barrier for a Multilingual Africa^[1] includes a Fellowship for Low Resource African Languages in order to develop datasets and strengthen capacities and innovation by building specific datasets^[2]:

1. Ewe language^[3] and Fongbe language^[4] parallel text dataset for NM Translation
2. Yoruba language^[5] Machine Translation dataset
3. Chichewa language^[6] document classification datasets
4. Wolof language^[7] (link) text-to-speech dataset for
5. Kiswahili language^[8] (link) document classification datasets
6. Tunisian Arabizi language^[9] sentiment analysis dataset
7. Swahili: News Classification Dataset^[10]
8. Twi language^[11]
9. Luganda language^[12]

Result 2: African Language Dataset Challenges

These AI4D datasets are further made into competitions of five NLP challenges hosted on Zindi as part of this AI4D’s ongoing African language NLP project, which is a continuation of the African language dataset challenges we hosted in 2020^[13]. The competition engagement in the challenges shows a total of 153,926 unique page views across 111 countries for the implementation of the challenges: Tunizi Arabizi, Yoruba, Ewe & Fongbe, Chichewa. Wolof went live on 12 Feb 2020 so statistics are not available yet. The current statistics of each challenge are the following:

• AI4D iCompass Social Media Sentiment Analysis for Tunisian Arabizi objective^[17]  (20 November 2020—29 March 2021): 539 Data scientists enrolled, 213 Data scientists on the leaderboard, 3 630 Submissions, Accuracy score: 0.94
• AI4D Malawi News Classification Challenge challenge^[18] (22 January—10 May): 218 Data scientists enrolled, 69 Data scientists on the leaderboard, 686 Submissions, Accuracy score: 0.64
• AI4D Takwimu Lab – Machine Translation Challenge challenge^[19] (18 December 2020—26 April 2021): 134 Data scientists enrolled, 11 Data scientists on the leaderboard, 142 Submissions, BLEU score: 0.35
• AI4D Yorùbá Machine Translation Challenge challenge^[20] (4 December 2020—12 April 2021): 314 Data scientists enrolled, 33 Data scientists on the leaderboard, 285 Submissions, BLEU score: 0.43
• AI4D Baamtu Datamation – Automatic Speech Recognition in WOLOF challenge^[21]  (12 February—24 May)

Result 3: Speech-to-text Platform for African Languages

The objective of this third part of the project is to build a Wolof text-to-speech system, to be extended into a general platform for all African languages as part of the Masakhane platform. The project will exploit a dataset of 40000 Wolof phrases uttered by two actors. This open-source dataset is a deliverable of a previous project. The project will be conducted following four phases:

1. Evaluation of the quality of the dataset
2. Implementation of a machine learning model mapping Wolof texts into their corresponding utterances
3. Quantitative and qualitative evaluation of the implemented model’s performances
4. Development of an API exposing  implemented text to speech model

What are we specifically trying to achieve?

Advances in speech and language technologies now enable tools such as voice search, text-to-speech, speech recognition and machine translation. These are however only available for high resource languages like English or Chinese.

Without foundational digital resources for African languages, which are considered low-resource in the digital context, these advanced tools remain out of reach. This work details the AI4D – African Language Program, a 3-part project that will:

1. Incentivise the crowd-sourcing, collection and curation of language datasets through an online quantitative and qualitative challenge
2. Support research fellows for a period of 3-4 months to create datasets annotated for NLP tasks
3. Host competitive Machine Learning challenges on the basis of these datasets.

How do Language Technologies relate to UNESCO?

Languages, with their complex implications for identity, cultural diversity, spirituality, communication, social integration, education and development, are of crucial importance for people, prosperity and the planet.

People not only embed in languages their history, traditions, memory, traditional knowledge, unique modes of thinking, meaning and expression, but more importantly they also construct their future through them.

In this context, Language Technologies (LT), greatly contribute to the promotion of linguistic diversity and multilingualism. These technologies are moving outside research laboratories into numerous applications in many different areas. UNESCO’s International Conference Language Technologies for All (LT4All): Enabling Linguistic Diversity and Multilingualism Worldwide, organized in December 2019, underlined spelling/grammar checkers up to speech and speaker recognition, machine translation for text and audio, speech synthesis, and spoken dialogue among others as important areas for enabling linguistic diversity and multilingualism.

In addition, the Los Pinos Declaration on the Decade of Indigenous Languages (2022-2032) calls for the design and access to sustainable, accessible, workable and affordable language technologies and places indigenous peoples at the centre of its recommendations under the slogan “Nothing for us without us.”

What is the African Languages Programme?

The AI4D – African Language Program was conceptualized as part of a roadmap to work towards better integration of African languages on digital platforms, in aid of lowering the barrier of entry for African participation in the digital economy. It was organised in 3 key phases.

Phase 1: Language Dataset Challenges

The AI4D Language Dataset Challenges were framed to focus on data collection, in response to the challenges of low availability of input data for African languages and the poor discoverability of resources that do exist, thus hindering the ability of researchers to do machine translation [14], and other NLP tasks.

We put together a panel of judges and performed both qualitative and quantitative evaluations, based on datasheets [2] submitted with each datasets and the datasets themselves, to identify outstanding submissions. The two rounds of this challenge yielded 52 dataset submissions with 13 winning prizes.

Some of the key observations identified from this phase of the project include [3]:

• Teams composed of individuals from relevant multi-disciplinary backgrounds, including computer scientists, professional translators and linguists, were able to create and annotate datasets that captured fundamental lexical and semantic nuances of languages.
• The challenge framing allowed for anyone to participate. While useful as an exercise in evaluating the interest in such a challenge, high quality submissions came from teams who had been exposed to NLP research work.
• Since the challenge was evaluated monthly, we often received disparate submissions from the same teams. Instead, one large dataset built over a couple of months would have been the ideal outcome.

These observations motivated the design of a subsequent phase of the project, the Fellowship.

Phase 2: Language Dataset Fellowships

From the top teams that participated in the challenges, we invited nine to take part in a subsequent phase of the program, a 3-4 month Fellowship Program. This provided research grants for teams to invest in resources, gave enough time for collaborative consultations to determine the sizes of expected datasets, the downstream NLP tasks they would be annotated for as well as mentorship and advisory requirements.

The datasets developed through this process cover a variety of languages and NLP tasks; Machine Translation datasets (Ewe, Fonge, Yoruba, Luganda, Twi and the 11 official languages of South Africa), a Text-to-Speech dataset (Wolof), a Sentiment Analysis dataset (Tunisian Arabizi), a Keyword Spotting dataset (Luganda) and Document Classification datasets (Chichewa and Kiswahili).

The Fellowship also presented a platform to tackle some opportunities identified to support future work in African, and low resource, language dataset creation, including research and analysis of the legal implications of obtaining textual, visual and audio data from a variety of online sources, and the development of copyright, intellectual property and data protection guidelines for NLP researchers.

These guidelines will be published in addition to research papers from the individual fellows on their particular dataset development work.

Phase 3: Machine Learning Competitions

This final phase, which is still in progress, will involve design and split of the datasets into train, development and test sets; the preparation of datasheets to document the motivation, composition, collection process and recommended uses of the datasets and hosting ML competitions on Zindi, an African data science competition platform, so as to engage the wider NLP community.

What is our research ambition?

 The objective of this work is to create good quality African language datasets for a variety of Natural Language Processing and Speech Processing tasks. This work is in support of Masakhane, a research effort for NLP for African languages. Masakhane as a resource is open source, continent-wide, distributed and online. It is a community of researchers working on a wide variety of African languages, some of whom are funded to build datasets via this AI4D fellowship.

Some of the challenges for the development of NLP for African languages identified by researchers in Africa include (Martinus and Abbott 2019):

• Low availability of resources (input data) for African languages that hinders the ability for researchers to do machine translation.
• Discoverability: The resources for African languages that do exist are hard to find. Often these resources are not available under open access licenses thus reducing the ability of research institutions to work together and share knowledge on language datasets to strengthen innovation.
• Reproducibility: The data and code of existing research are rarely shared, which means researchers cannot reproduce the results properly.
• Lack of benchmarks: Due to the low discoverability and the lack of research in the field, there are no publicly available benchmarks or leader boards to compare new machine translation techniques to old ones.

What kind of impact do we want to achieve?

 The project aims at addressing some of the challenges identified above through:

• Development of datasets of African languages that maybe used in some countries only or have transboundary usage that can be used for strengthening access to information and spur innovation based on NLP technologies
• Enhancement of capacities among young researchers for the development of open languages datasets and language tech applications through development of guidelines and training through open educational resources in collaboration with national institutions
• Development of a multi stakeholder network for strengthening research on language technology based on AI techniques for African languages

How do we plan on implementing this project?

Supported by AI4D-Africa and the University of Pretoria’s Data Science for Social Impact Research Group and Knowledge 4 All Foundation, the first month will involve consultations alongside the teams to determine a number of factors:

• Dataset
  • Language
  • Downstream task scoping
  • Expected sizes of datasets
  • Preparation and documentation process
• Deliverables
  • Monthly targets and check-ins for guidance
  • Workshop/Conference paper for publication documenting the process

The teams will then have 3 months to further flesh out their datasets, after which, they will be expected to prepare write-ups of their research for publication. Once the dataset creation phase is complete, the datasets will be used in ML challenges hosted in Zindi and evaluated on the downstream task that each dataset has been prepared for. UNESCO funding will be used to promote the work of Fellows 1-5, and GIZ funding will be used to promote Fellows 6-8.

Who are the Fellows?

• Fellow 1: Amelia Taylor – Chichewa language
• Fellow 2: Takwimu Lab – Fongbe and Ewe language
• Fellow 3: David Adelani – Yoruba language
• Fellow 4: Baamtu Datamation –Wolof language
• Fellow 5: iCompass – Tunisian Arabizi language
• Fellow 6: Davis David – Kiswahili language
• Fellow 7: Ari Ramkilowan and Masabata Mokgesi-Selinga – the 11 national South African languages
• Reseach Fellow 8: Makerere University – Luganda language
• Fellow 9: GhanaNLP – Twi language

What are the partners involved?

This work has been sponsored through a partnership between several organisations, listed below in non-alphabetical rder;

• Knowledge 4 All Foundation
• AI4D Africa Initiative
• Zindi platform
• Data Science for Social Impact Research Group, University of Pretoria (DFSI)
• Centre for Intellectual Property and Information Technology (CIPIT), Strathmore University
• United Nations Educational, Scientific and Cultural Organization (UNESCO)
• GIZ – Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH
• IDRC – International Development Research Centre
• UNESCO Chair in Artificial Intelligence at University College London

References

[1] Cracking the Language Barrier for a Multilingual Africa https://www.k4all.org/project/language-dataset-fellowship/

[2] African Natural Language Processing https://zenodo.org/communities/africanlp/?page=1&size=20

[3] Ewe language https://www.k4all.org/project/ewe-database/

[4] Fongbe language https://www.k4all.org/project/database-fongbe/

[5] Yoruba language https://www.k4all.org/project/database-yoruba/

[6] Chichewa language https://www.k4all.org/project/database-chichewa/

[7] Wolof language https://www.k4all.org/project/database-wolof/

[8] Kiswahili language https://www.k4all.org/project/database-kiswahili/

[9] Tunisian Arabizi language https://www.k4all.org/project/database-tunisian-arabizi/

[10] Swahili language https://www.k4all.org/project/database-kiswahili/

[11] Twi language https://www.k4all.org/project/twi-language/

[12] Luganda language https://www.k4all.org/project/luganda-language/

[16] Zindi and AI4D build language datasets for African NLP https://zindi.medium.com/zindi-and-ai4d-build-language-datasets-for-african-nlp-34a4d0ea129

[17] AI4D iCompass Social Media Sentiment Analysis for Tunisian Arabizi https://zindi.africa/competitions/ai4d-icompass-social-media-sentiment-analysis-for-tunisian-arabizi

[18] AI4D Malawi News Classification Challenge https://zindi.africa/competitions/ai4d-malawi-news-classification-challenge

[19] AI4D Takwimu Lab – Machine Translation Challenge https://zindi.africa/competitions/ai4d-takwimu-lab-machine-translation-challenge

[20] AI4D Yorùbá Machine Translation Challenge https://zindi.africa/competitions/ai4d-yoruba-machine-translation-challenge

[21] AI4D Baamtu Datamation – Automatic Speech Recognition in WOLOF https://zindi.africa/competitions/ai4d-baamtu-datamation-automatic-speech-recognition-in-wolof

[1] Martinus, J. Webster, J. Moonsamy, M. S. Jnr, R. Moosa, and R. Fairon. Neural machine translation for south africa’s official languages. arXiv preprint arXiv:2005.06609, 2020.

[2] Gebru, J. Morgenstern, B. Vecchione, J. W. Vaughan, H. Wallach, H. Daumé III, and K. Crawford. Datasheets for datasets. arXiv preprint arXiv:1803.09010, 2018.

[3] Siminyu, S. Freshia, J. Abbott, and V. Marivate. Ai4d–african language dataset challenge. arXiv preprint arXiv:2007.11865, 2020.

Partners

Disclaimer

The designations employed and the presentation of material on these map do not imply the expression of any opinion whatsoever on the part of the Secretariat of the United Nations concerning the legal status of any country, territory, city or any area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Final boundary between the Republic of Sudan and the Republic of South Sudan has not yet been determined. Final status of the Abyei area is not yet determined.

Foreword

Gender Equality is the 5^th United Nations Sustainable Development Goal (SDG). As with other SDGs, Artificial Intelligence can play a role in promoting good practices, or, to the contrary, can enhance the existing biases and prejudices. A recent workshop at IJCAI, in Macao, made the case for a number of projects relating SDGs and Artificial Intelligence. In order to push forward the questions relating AI and gender equality, the Knowledge for All foundation, the Centre de Recherches Interdisciplinaire de Paris and the Unesco Chair on OER at Université de Nantes jointly organized this one day workshop. The workshop was built around sessions on the different aspects of the question. We were glad to give a special status to our keynote speaker, Bhavani Rao, from Amrita University, Director of the Ammachi and holder of the Unesco Chair on « Women’s Empowerment and Gender Equality ».

The questions identified with the help of our program committee were the following:

• Bias issues: typically, AI will reproduce the bias in the data. If the data contains a prejudice, the decision making based on AI can reproduce (and sometimes enhance) that prejudice.
• Gender issues in AI projects. Is it a good idea to add a “gender officer” to an AI project? Someone who can look out so that prejudice doesn’t creep in?
• AI for Education: how educating women can make special sense? What do we need to look out for?

But, as the following workshop notes show, the discussion allowed us to reflect upon many different aspects too.

Colin de la Higuera

UNESCO Chair on Technologies for training teachers with open educational resources

Université de Nantes

Download the report here or scroll down to read

About the workshop

The workshop took place at the Centre de Recherches Interdisciplinaires at the CRI – Learning center extension from 10am to 5pm and was advertised through the websites of the different partners organizing the event (UNESCO Chair at Université de Nantes; Knowledge for All Foundation and CRI). It was advertised online.

This meeting built upon work done by a number of partners, concerning gender issues in teaching computing, fair representation of women by AI and more broadly the impact of AI on the United Nations Sustainable Development Goals (SDGs).

These questions correspond to the 5^th SDG and it is already known that AI can both increase the effect of bias or correct it, depending on how it is deployed.

Colin de la Higuera, Université de Nantes, Unesco Chair in Teacher Training Technologies with Open Education Resources set the scene, explained how this workshop was linked with the previous workshop organized by the Knowledge for All foundation in Macau in July 2019. He acknowledged the help of the CRI, the Unesco, Université de Nantes, the Knowledge for All Foundation and the Société informatique de France in organizing the event.

Bhavani Rao, from Amrita University, Director of the Ammachi Labs (a computer human interaction lab) and holder of the Unesco Chair on « Women’s Empowerment and Gender Equality » presented the initiatives led in India and the spirit of the work done around these questions in India, also involving Human Computer Interfaces and Artificial Intelligence. She explained how they have used artificial intelligence to map the various factors contributing to women’s vulnerability across India and to identify “hot spots” and “cold spots” for women’s empowerment. These identified locations take into account more than 250 quantitative data measurements in combination with qualitative data to represent a comprehensive understanding of the state of empowerment at that location. Bhavani Rao emphasised the need to track and monitor the progression of the women involved in Ammachi Labs’ (or any, for that matter) vocational training programmes and evaluate the impact it has on their community. Furthermore, she advocated in favour of a holistic approach and warned against initiatives that are just aimed at solving isolated issues, as often there is unintended fallout that negatively impacts both women and their communities.

John Shawe-Taylor, Unesco Chair in Artificial Intelligence at University College London, presented the different interventions that have been implemented at UCL toward gender equality in a Computer science department. These can be summarized as the 4 As: 1) Arrive: encouraging girls to study computer science, 2) Aspire: creating a supportive environment, 3) Achieve: ensuring they realise their full potential, 4) Advance: ensuring equal opportunities for career progression. The talk also highlighted a number of ways in which AI enabled systems might further accelerate the effectiveness of these interventions.

Wendy MacKay, INRIA National Institute for Research in Computer Science and Automation, Situ Ex talked about her own experience as a woman and a researcher. She also insisted on the importance of a user oriented approach: keeping the user in the loop at the different times of the development of an AI project could help humans develop and learn alongside AI.

Prateek Sibal, co-author of Unesco publication “Steering AI for Knowledge Societies” highlighted that while technological artefact may be neutral the culture and the practices associated with its use are biased against women. He discussed how different AI based tools including facial recognition and digital voice assistants mirror biases in society. For instance, several labelled image data sets used for training facial recognition programmes have high error rates in recognising gender of dark skinned women as compared to men. He pointed out that deep fakes based on Generative Adversarial Networks (GANs) are overwhelmingly used to target female celebrities by creating pornographic videos. He raised concerns around ‘technological determinism’ and advocated for an approach to the development and use of AI that is human centred and is anchored in human rights and ethics. He demonstrated how some instances of use of facial recognition technology are violative of human rights violations, can have life threatening consequences for people with diverse sexual orientations and gender identities. Vigilance by researchers, civil society and governments is enabling detection of bias in AI systems, this presents an opportunity to influence the culture of technology by developing artefacts that are gender equal, that respect diversity or even obliterate gender differences as was demonstrated with the example of gender neutral digital voice assistants.

A discussion with the room followed. Some of the ideas expressed during the debate were:

• A goal is to design interventions and avoid undesired side effects: ideally one might need a simulator? even better, a causal model? Can we consider randomized controlled trials?
• What if we improve women’s life in an otherwise unchanged world? This can turn for the bad. This being the key point made during Bhavani Rao’s talk.

Michèle Sebag, from CNRS (Centre national de la recherche scientifique) and Univ. Paris-Saclay discussed some thoughts about biases, glass ceilings, and how to handle them. Even after a 1st glass ceiling has been overcome (e.g. for women in selective engineering schools), biases remain as to the choice of options, with an impact on careers, money, etc. Even more puzzling, the wording of an option makes a significant difference regarding women’s choice (e.g. Energy for XXIst century vs Challenges of Sustainable Development) despite their technical content being 95% the same: the bias is in the language (footnote: nothing unexpected, see Bourdieu). As both genders might be using two different languages, a debiasing action might be to build translators; and/or display the gendered versions conveying the same content. This would also be fun! which is an important part of effective actions. [Using AI to reinforce self-confidence is another great perspective; note however that undermining the self-confidence feeds a multi-billion dollar industry].

Frédérique Krupa, Human Machine Design Lab, presented her own trajectory in the field and how, for her PhD on Girl Games: Gender Design and Technology, she studied belief systems as the principal influence amongst numerous factors in encouraging boys and discouraging girls to be interested in technology and pursue a career in ICT. The family factor is still today determining things far too strongly: through early choices by the parents (or the family environment) little girls were being deprived of the exciting activities and only getting access to less interesting, less challenging, less time consuming technological experiences. She followed up on her machine learning postdoc at 42, noticing the absence of interest on the quality, accuracy and representativity of data amongst homogenous teams of coder, mostly male, white, straight and from upper social classes, so that they do not consider these questions in their quest for optimal performance and chances of publishing – because they are not likely to suffer from bias. The issue of data quality is about having contextual information available to determine what bias may be present in the data and/or its resulting model. She calls for the development of AI UX practices, developed from quantitative social science methods.

A discussion with the room followed with some points:

• Detecting known biases is a hot topic in AI (gender, race, wealth, sexual orientation…). But what are the unknown biases? Building experiments to provide evidence for biases: defines a challenge to be tackled with psychologists, neurosciences, MLers;
• Another topic is ethical recommendation; to de-bias the recommendation one should have an idea of the targeted ultimate fair distribution. This is a normative issue: we need (on the top of all others) lawyers, politicians, citizens, …, sci-fi writers,…

Sophie Touzé, VetAgro Sup-Université de Lyon, and past President of the Open Education Consortium presented an original approach and offbeat vision of AI and the warning role it represents. The AI forces us to look at the skills unique to humanity, our added value in relation to the intelligence of machines. By challenging us, it provokes change and the saving awareness to know what we need to teach at school and university.

She insisted on what skills are essential. The 4 Cs are Collaboration, Communication, Creativity and Critical thinking and the 3 Ss are self-awareness, self-motivation and self-regulation. Unfortunately, these skills are not taught at school nor university. An app could be developed to help individuals monitor and develop these critical skills throughout their lives.

Empowered by these skills, each citizen of the world could participate to forge consciously the future we want no more as individual but as the human species.  The narrative of humanity should not be left in the hands of a few people that present to us as heroes. It’s time for women to participate writing humanity’s epic story together!

Sophie Touzé concludes with “We are the heroes we’ve been waiting for”.

Mitja Jermol, Unesco Chair on Open Technologies for OER and Open Learning, used his experience in AI based education projects to present what an education to AI could be. He made the point that there are 3 issues here: 1. Developing AI, 2. Using AI and 3. Interacting with AI. Most discussions today are related to increasing the knowhow in developing AI which includes two very specific domains namely mathematics and software programming. The fact is that AI has become mature enough to be taught to other domains as tolls to be used. This is why the education should be concerned with the 2 last ones. Like Sophie Touzé he insists on the importance of soft skills. He also describes some projects related to the question in which he is involved, such as the X5-GON project. Opening education, free and inclusive access to all through a global open education could be a strong mechanism to empower not just women but any individual in the world. AI plays a major role in this by understanding the complex system of materials, competences, infrastructure and the needs of particular individual.

Conclusion

As a conclusion, it was reflected that these questions should be further discussed. Colin de la Higuera believed that there were 2 different issues which had been at the core of the discussions of the day. 1. The issue of gender equality which is just as present in the field of AI as in other fields: female researchers are finding it difficult to emerge and only those strong, or –as Frédérique Krupa remarked- who don’t follow the rules, will make it. Yet everyone agrees that a more equal representation in the field is necessary. 2. The second issue is the effects of AI itself towards gender (in)equality, women vulnerability or women empowerment.

Actions to follow are to push the findings of this workshop forward in Unesco and elsewhere. Furthermore, the Knowledge for All Foundation will also build upon these discussions.

A report delivered by K4A trustees for UNESCO reporting on Artificial intelligence (AI) which is taking an important part in our lives, the question of educating towards AI becomes increasingly relevant. We argue in this document that although it may be premature to teach AI, we recommend an education to five pillars or core questions which should be of great use in the future:

1. Data awareness or the capacity of building, manipulating, visualising large amounts of data;
2. Understanding randomness and accepting uncertainty or the ability to live in a world where models cease to be deterministic
3. Coding and computational thinking or the skills allowing each to create with code and to solve problems through algorithms
4. Critical thinking as adapted to the digital society and finally
5. A series of questions amounting to understand our own humanity in view of the changes AI induces.

Download the report here or scroll down to read

Introduction

Artificial intelligence has been described as the new electricity. As such, the belief that it will have a profound influence over many fields, of which Education, is widely shared. For instance, in its 2018 report on artificial intelligence, the French committee chaired by Cedric Villani [25] presented Transforming Education as the first “focus”. In [12], hundreds of applications of AI have been scrutinised and mapped to the relevant technologies.

More recently, the JRC report The Impact of Artificial Intelligence on Learning, Teaching, and Education, by Ikka Tuomi et al. [20], considered the different aspects of the questions relating artificial intelligence and learning. And more generally, the question of transforming education with the help of technology is addressed by the Sustainable Development Goal 4 adopted by the United Nations in September 2015, and also by the OECD [13].

In this report we study the different interactions between AI and Education with an emphasis on the following question: If we accept that artificial intelligence is an important element in tomorrow’s landscape, what are the skills and competences which should appear in the future curricula and how can we help to train the teachers so that they can play the required role?

This report is one of the first addressing these questions: as such it is less built as a synthesis of existing reports with an increment from previous works than as an analysis based on the experience of teachers, researchers, academics and practitioners. A recent exception is the work by the UNESCO itself who has been exploring the links between AI and education [15].

What is AI? Why is the issue of general interest?

The history of artificial intelligence goes back to the history of computing. Alan Turing was interested very early in the topic of machine intelligence [21]: some of the ideas he introduced 70 years ago are still extremely relevant today; he argued in favour of randomness and discussed the implications of machine learning to society. Even if Turing didn’t predict the importance of data, he did understand the machine’s capacity of learning would be key to machine intelligence.

Another of Turing’s contributions to artificial intelligence is what became known as the Turing test: in this test an external (human) examiner has the capacity of interaction with both a machine and a human, but the interface being mechanical, he will have to examine the answers to the interactions rather than their form.

The examiner’s goal is to distinguish man from machine; the goal of the artificial intelligence is to confuse the examiner. This leads to the very general definition of artificial intelligence still in use today where it is less about a machine being intelligent than about a machine being able to convince the humans that it is intelligent.

The official birth of artificial intelligence is usually associated with the Dartmouth Summer Research Project on Artificial Intelligence: in 1956 researchers met in Dartmouth College to address the difficult questions for which computing failed to contribute [11].

Today, because of the impact of Machine Learning, and most notably of Deep Learning, alternative definitions for artificial intelligence have been considered: a more business oriented view is that AI matches these deep learning techniques which have a strong impact on industry [12, 30].

Being able to pass the Turing test is no longer the shared goal of research and would not explain the impact of AI today. Today’s successes of AI depend on several factors including machines tailored to the needs of the algorithms and the massive increase in quantity and quality of data. Machine learning techniques work today much better than 10 years ago.

They build better models, make less errors in prediction, they can make good use of the huge volumes of data, are able to generate new realistic data, and are being tuned and adapted to an increasing variety of tasks. As such, these algorithms are no longer aiming at tricking the human in believing that they are intelligent; they are actually replacing (in part) the human in one of her more intelligent tasks: that of building algorithms.

If computing is about algorithms and data, modern AI is a data science: it relies on being able to handle and make the most from data. Whereas the natural trend for computing was to build algorithms to handle data, me may argue that artificial intelligence is about data building the algorithms that build algorithms.

Why understanding AI matters

Artificial intelligence is influencing all parts of society where data can be made available and where there is room for improvement, either by automatising or by inventing new challenges and needs. In substance, this means that every human activity is being impacted or can be impacted.

For instance, all 17 United Nations sustainable development goals (SDG) are currently being scrutinised by AI experts [8]. The use of AI can lead to complex new situations, which can only be understood through an actual understanding of the technical and conceptual aspects underlying it. In many cases our physical understanding of the world is insufficient to gauge the impact or even the opportunity for AI.

When we read for i= 1 to 1000000 intuition is of little use: no human does anything 1 000 000 times in a lifetime! The mathematical world and its full abstraction doesn’t give an adequate answer either. People may imagine artificial intelligence as a process by which a machine does things the same way as we (humans) do, only faster and with more memory, storage space or computation power. But AI doesn’t always work that way. The algorithms will not follow the patterns from our physical world and an understanding of what they do will not give us a realistic idea of why they work and why they don’t.

When it comes to training teachers, that leaves us with two approaches: the first one supposes the teachers should be able to actually build simple AI systems: they should know how to code and be able to assemble blocks in order to obtain more complex systems, run artificial intelligence algorithms, build models and use them. The second approach supposes people do not learn how to design but only how to interpret and use. They will then necessarily interpret things through their very limited own physical world values.

AI and Education

The links between AI and Education are not new. They have worked in both directions, but one of these has received, up to today, much more attention than the other [15].

AI for Education

The first conference on Artificial and Education dates back to more than 20 years ago: the challenges have since been wide ranging and are now addressed by strong multidisciplinary communities. Research projects have been funded by the European Union, Foundations and individual countries. The goal is to make use of artificial intelligence to support education. An emerging industry has developed, covered by the name Edutech (which isn’t strictly AI) and the question has been studied in a number of reports: [23, 15, 25, 20].

Education for AI

Whereas the question of educating everyone to artificial intelligence is new, the one of training experts for industry has been dealt with for some time. Artificial intelligence has been taught in Universities around the world for more than 30 years. In most cases these topics require some strong foundational knowledge in computer science and in mathematics.

The increasing importance of data science, artificial intelligence and machine learning is currently leading to a modification of the computing curricula [16]. Education to artificial intelligence, prior to University, is in 2019 a whole new question. If it has not appeared before, this can be due to several reasons:

• The need is novel: if AI has been around for some time, applications for everyday life have been limited. Today, through mobile phones or ambient companions, interaction with AI occurs in a routine way, at least in the more industrialised countries [12].
• The issues surrounding education as a global question are huge and AI may not be perceived as essential [14], even when the goal is that of studying education in a digital setting [22]. The efforts made in many countries to introduce an Information and Communication Technologies curriculum have not even been able to produce yet results, and, with AI, it seems we are asking for more today [24]!
• It is difficult to introduce the topic without trained teachers. As an example, in 2018, the French government decided to add a new computing curriculum at high school level (16-18 year old students) with the ambition of introducing artificial intelligence. This was impossible due to the numerous resistances.
• It is still unclear what should be taught if we consider a teaching which should have some form of validity over the years. What will artificial intelligence be in 20 years, or even 10 or only 5?

Why should we train teachers in AI?

AI applications are going to be present in all areas [12]. As such, one could just add the usage of the applications to the individual skills required by a teacher. But this would probably limit in many ways the capacity of the teachers to adapt to new applications. One can also state that the children are going to be brought up in a world where artificial intelligence will be ambient and can therefore be called AI natives [26]. Should a teacher just know about the key ideas of AI? Or should she be more aware of other questions?

Is teaching AI an enhanced version of teaching computational thinking?

Teaching coding and computational thinking has been advocated strongly since 2012 [28, 7]. Many countries have now introduced the topic. Learning to code isn’t just about acquiring a technical skill: it is about being able to test one’s own ideas instead of just being able to run those of someone else. And of course, there is a strong dose of ideas in artificial intelligence, which means that both knowing how to code can help one use AI in a creative way and also understand the underlying questions and concepts. AI is an extension of computing. It comprises it but also introduces some new ideas and concepts.

Why should a teacher be AI aware?

Many of the reasons for training the teachers to some understanding of AI are very close to those advocated to prepare them to digital skills [24]. Whereas a first goal is to make sure that teachers are digitally aware, and this goal is not reached yet, how important/urgent is it to be AI aware? Why would that be necessary? Let us discuss some reasons.

The role toward community. If artificial intelligence is to impact every aspect of society, as many are predicting, citizens and future citizens are going to require guiding, some help to understand, decrypt these technologies.

In order to train skilled learners. An aspect put forward by many analysts is the impact of artificial intelligence on jobs. The more optimistic analyses point out that where many jobs will be lost to robotisation, new jobs will emerge.

And even if these jobs seem to require soft skills, it would seem reasonable that many will be linked directly or not with the technical aspects of AI. If in 2019 it seems neither relevant nor possible to train every child to AI, it would on the other hand be necessary that each child be given the principles and bases allowing her to adapt and learn at a later stage.

Because the learning environment is changing and will include AI. Intelligent tutoring systems, tools which will allow to propose individualised learning experiences, tailored companions… are some of the projects under way which will necessarily lead to situations where the learner is helped. Understanding these tools will be an asset, if not a necessity.

Because AI is a valuable tool for teaching. AI is used today to help the teacher. For example, project X5-GON recommends open educational resources adapted to the needs of a particular teacher [29]. In the same way as today a teacher is penalised by not being able to make use of the digital tools available, tomorrow’s teacher may lose out if she cannot access AI tools in a simple way.

Towards a curriculum. The proposed five pillars.

Artificial intelligence has not reached its maturity. The topic as it was defined in 1956, studied during 40 years, reached its spectacular results since 2012 is still difficult to understand. It is even more difficult to forecast how the technologies will evolve, even in a close future. If building a full curriculum is beyond the reach of this document, it is possible to put forward five pillars and propose to build upon these. We represent our proposal in Figure 1: we believe five pillars or core questions should be added to the training of teachers (lower part of the figure) and on these, in due time, AI would be able to be taught (top of figure).

Uncertainty and Randomness

Data is inconsistent. It does not demonstrate a strict causal nature. With data, a same cause can lead to different effects. Dealing with this legitimate non determinism in the modelled world, which is going to be used for AI based decision making, requires the acquisition of alternative skills. Probabilistic reasoning and statistics will need to be taught, but before that, activities allowing children to understand the stochastic nature of most modelled processes and those encouraging to make the best use of the imperfections of the data are necessary.

Yet AI both also means a new form of determinism which deserves our attention: when predictive systems are taken (too) seriously and we are told that our child aged 1 will develop into a scientist or have a complicated social life through a misuse of data, not understanding how these predictions work can cause a lot of damage.

An understanding that the forecasts proposed by AI are not ground truths, but estimations, and how these are to be interpreted is of great need. Teaching this may be complicated for didactic reasons: accepting uncertainty also means teaching without implying that the teacher is omni-scient and makes no mistakes.

Coding and Computational Thinking

Coding and computational thinking are today in the curricula of many countries, following the recommendations of experts [10, 18]. In many cases the AI code it is about using libraries for the programming languages which allow us to manipulate large amounts of data with very few instructions. But a proper usage of these techniques does involve some coding skills [19, 28, 6].

Furthermore, it has been argued that expert users of AI (for example the doctors) will need to understand the algorithms in order to know when not to trust the machine learning decision. Efforts have taken place in different countries to address this question and the related question of training the teachers [10]. In France, project Class’Code [5, 4] relies upon Open Educational Resources to allow teachers and educators to learn. Computers and robots are obvious artefacts, but an alternative approach is that of Computer Science Unplugged [2].

Data Awareness

AI is going to rely on data. Whereas the algorithm is at the centre in computing, this is much less the case with AI, where, often, most of the effort will concern the collection, preparation and organisation of the data [16]. An education to data (science) will rely on activities where data is collected and visualised, manipulated and analysed [15]. As a side effect, large amounts of data justify that algorithms get taught with more care as testing becomes much more complex.

Critical Thinking

Social sciences can and should contribute with many of the ethical questions AI raises. Critical thinking is one important aspect but it is essential that it relies on a real understanding of the way technology works.

A typical example: when attempting to detect fake news and information –a truly important question– it is often suggested that the answer consists in finding the primary source or in relying on common sense. This is a great 20th century answer but is of less use in many situations on the web from AI generated texts, images or videos. Today, the answer comes through a combination of understanding the digital literacy concepts and being able to make use of algorithms to build one’s convictions. In other words, the human alone is going to find it difficult to argue with a machine without the help of another machine.

Yet it would be just as much a mistake to only teach the technology without giving the means to understand the impacts, evaluate the risks and have a historical perception of media. In most reports on AI there is an agreement that an analysis of the ethical implications should be taken into account before deploying. Researchers from Media Sciences have worked on the question for some time [27] and should be encouraged to work with AI specialists.

Post AI Humanism

The previous 4 pillars can be matched to existing competences, skills and teaching profiles. The one we introduce now may be more difficult to fit in. The key idea is that the progress of AI is making us, as human beings, reconsider some ground truths. It is already known that our interaction with technology has an impact on non technological attitudes: for examples, teachers agree that the children’s use of the smartphone and the specific type of multitasking is introduces has an effect
on their capacity of studying, at least in the formal settings proposed by schools. With artificial intelligence, these changes may be even more formidable. We introduce this idea through four examples.

Truth

In 2017, system Libratus was built by researchers at Carnegie Mellon University [3]. This system beat some of the best poker players in the world. For this, the system used reinforcement learning to build its optimal policy. This includes the fact that it learnt to bluff -a necessary skill in poker without being explicitly trained to bluff. But Libratus did learn that the better strategy to win was to lie from time to time. In other words, the system was trained to win. And if this included bluffing –which is a socially acceptable form of lying– it did just that.

Experience

In 2016 system Alphago beat go champion Lee Sedol by four victories to one [17]. The system, like most till then, made ample use of libraries containing thousands of games played by experts: the machine built its victories on top of the human history and knowledge. A few months later, the new version called Alphago-zero was launched, beat Alphago by 100-0, and then was adapted to chess. The main difference was that Alphago-zero discarded all the human knowledge, just used the rules of the game and the capacity of the machine to learn by playing against itself. The question this raises is: do we need to build society upon its experience?

Creativity

This question is regularly posed. It can matter legally and intellectually. Today, artificial intelligence can compose music, write scripts, paint pictures, modify our photographs. Through artificial generation new artefacts can be created. It should be noted that in such cases where artificial intelligence is used for artistic creation it is most often reported that a human artist was part of the project. Whilst this may be in part true, it may only be that we need to be reassured.

Yet, again in the area of games, it is interesting to see specialists comment online the games played by the latest artificial intelligence programs. Whereas some years ago the “brute force” of the machine was put forward, this is much less the case today: the creative nature of the moves is admired by the human grand-masters. A question raised here is: can a machine create without feeling nor conscience? One answer is to say that the result is what matters: if we believe there is creation, do we need the feeling? [9].

Intelligence

Intelligence itself is being impacted by the progress of AI. Each time a progress is made and machine beats man at something which up to now was considered to be an activity requiring intelligence, experts invariably announce that the given activity didn’t really need intelligence. More and more, the goal seems to define intelligence in such as way as to make it unachievable by a machine.

Extending the model

The pillars described in the previous section must be understood as being able to support a larger framework of competences the teachers and learners will need to master in order to use and create AI systems (see Fig. 1). But they can also be seen as self contained 21st century skills which would allow them better to make use of the technologies introduced by and with artificial intelligence.

Linking with the UNESCO ICT-CFT

AI will have to be taught by teachers. There is a big difference between the countries in teacher training, for example over the basic ICT skills we can rely on to be able to install an AI teaching agenda. As a uniform starting point and framework we take the approach promoted by the UNESCO, namely the UNESCO ICT-Competency Framework for Teachers (ICT-CFT), and reflected in a series of evolving documents (over the past 10 years) [24].

In [24], the 6 aspects of a teacher’s work are scrutinised with respect to a goal of making use of ICT for better teaching:

• A1 Understand ICT in Education: how ICT can help teachers better understand the education policies and align their classroom practices accordingly;
• A2 Curriculum and assessment: how ICT can allow teachers to better understand these questions but also intervene and propose new modalities;
• A3 Pedagogy: how the actual teaching itself can be positively impacted through the informed use of ICT;
• A4 Application of Digital skills: how to make use of the new skills acquired by the learners to support higher -order thinking and problem solving skills;
• A5 Organisation and administration: how to participate in the development of technology strategies at different levels;
• A6 Teacher professional learning: how to use technology to interact with communities of teachers and share best practices.

Each aspect is then analysed, regarding the impact towards ICT, following three stages: technology literacy (.1), knowledge deepening (.2) and knowledge creation (.3). The report raises two questions:

1. In what measure does the ICT-CFT offer a good framework for teachers to be trained to AI?
2. In what measure would the ICT-CFT benefit from the impact of AI?

How the ICT-CFT allows teachers to move on to AI

The ICT-CFT aims at allowing teachers to know how to use, in an experimented way, and to develop new ideas, learning material, curricula through ICT. As the different AI pillars presented in this paper all  rely on an understanding of how computers, algorithms, data works, the ICT-CFT will be an important stepping stone towards AI. Teachers who will be able to master the different tools, strategies and skills proposed in the ICT-CFT would be able to interact better with the questions raised by the proposed pillars. We represent, in Fig. 2, with a full line, the main contributions of the ICT-CFT aspects to the five pillars.

The impact of AI on the ICT-CFT

The arrival of new AI tools for education (like [29]) will probably help motivate better teachers to the usage of ICT: the advantages will become clearer and we can hope that the usage will be simplified. Typically, OER are today difficult to construct, to offer, to find. AI and related technologies should make them much more accessible, which would ensure their wider adoption.

On the other hand, a better understanding of the key questions raised by the proposed pillars will have a positive effect on the motivation of the  researchers to progress in levels across the different aspects proposed in the ICT-CFT.

For example, better understanding the social and ethical implications (Critical thinking and post AI humanism) would have a positive impact on the way teachers react and their motivations in training.

The ambitions proposed with the 5 pillars can also impact positively the ICT-CFT by requiring extra ambition: Coding and computational thinking are mentioned but not recommended, whereas for AI the proposal is that these are necessary skills. Learning to code would, on its own, render the ICT-CFT much more fulfillable.
We represent, in Fig. 2, with a dashed line, the main contributions of the five pillars to the ICT-CFT.

Artificial Intelligence and Open Educational Resources

ICT Teachers are very much the forerunners of sharing open educational resources (OER): as they naturally use the computer as an object of learning and a learning artefact, they have been very active in producing and sharing OER. This is also the case for AI: one can predict a great benefit for all.

Artificial intelligence is also working today at provided better tools to publish, share and access OER [29]. Therefore, the education of AI or towards AI proposed in this document should make ample use of OER.

Conclusion

We have presented in this preliminary report five competences or pillars which should be taking an increasing importance given the penetration of AI in society. Further work should follow, to better understand at what age and in what way the relevant concepts should be introduced, studied, mastered. around which we expect to explain how AI should be taught, both to teachers and to learners.

Acknowledgements

We would like to thank the following persons for their help and expertise: Neil Butcher, Victor Connes, Jaco Dutoit, Maria Fasli, Françoise Soulié Fogelman, Marko Grobelnik, James Hodson, Francois Jacquenet, Stefan Knerr, Bastien Masse, Luisa Mico, Jose Oncina, John Shawe-Taylor, Zeynep Varoglu, Emine Yilmaz.

References

[1] Fathers of the deep learning revolution receive ACM A.M. Turing award. News, 2019. https://www.acm.org/media-center/2019/march/turing-award-2018.

[2] T. Bell, I. H. Witten, M. Fellows, R. Adams, J. McKenzie, M. Powell, and S. Jarman. CS Unplugged. csunplugged.org, 2015.

[3] Noam Brown and Tuomas Sandholm. Superhuman AI for heads-up no-limit poker: Libratus beats top professionals. Science, 359(6374):418–424, 2017.

[4] Class’Code. Class’code, ses principes et leviers. Position paper, 2015. https://project. inria.fr/classcode/classcode-ses-valeurs/.

[5] Class’Code. Maaison : Maîtriser et accompagner l’apprentissage de l’informatique pour notre société numérique. Position paper, 2015. https://drive.google.com/drive/folders/0B42D-mwhUovqQ1RyOW01WUtyR1k.

[6] Informatics Europe. Informatics education: Europe cannot afford to miss the boat. Technical report, 2015. http://www.informatics-europe.org/images/documents/informatics-education-acm-ie.pdf.

[7] A. Fluck, M. Webb, M. Cox, C. Angeli, J. Malyn-Smith, J. Voogt, and J. Zagami. Arguing for computer science in the school curriculum. Educational Technology & Society, 19(3):38–46, 2016.

[8] Knowledge for All Foundation. IJCAI workshop on Artificial Intelligence and United Nations sustainable development goals. Workshop, 2019. https://www.k4all.org/event/ijcai19/.

[9] Yuval Noah Harari. Homo Deus: A Brief History of Tomorrow. Harvill Secker, 2016.

[10] L’Académie des Sciences. L’enseignement de l’informatique en France – il est urgent de ne plus attendre. Position document, 2013. http://www.academie-sciences.fr/fr/activite/rapport/rads_0513.pdf.

[11] John McCarthy, Marvin L. Minsky, Nathaniel Rochester, and Claude E. Shannon. A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955. AI magazine, 27(4):12–12, 2006.

[12] McKinsey. Notes from the AI frontier. insights from hundreds of use cases. Discussion paper, 2018. https://www.mckinsey.com/featured-insights/artificial-intelligence/notes-from-the-ai-frontier-applications-and-value-of-deep-learning.

[13] L. Nedelkoska and G. Quintini. Automation, skills use and training. Position document, 2018. Documents de travail de l’OCDE sur les questions sociales, l’emploi et les migrations, n 202, Éditions OCDE, Paris, https://doi.org/10.1787/2e2f4eea-en. https://www.oecd-ilibrary.org/employment/automation-skills-use-and-training_2e2f4eea-en.

[14] High Level Committee on Programmes. Towards a UN system-wide strategic approach for achieving inclusive, equitable and innovative education and learning for all. Report, 2019.

[15] UNESCO Education Sector. Artificial intelligence in education: Challenges and opportunities in sustainable development. Report, 2019.

[16] R. Benjamin Shapiro, Rebecca Fiebrink, and Peter Norvig. How machine learning impacts the undergraduate computing curriculum. Commun. ACM, 61(11):27–29, 2018.

[17] David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis. Mastering the game of go with deep neural networks and tree search. Nature, 529:484–489, 2016.

[18] The Royal Society. After the reboot:computing education in uk schools. Position paper, 2017. https://royalsociety.org/-/media/policy/projects/computing-education/computing-education-report.pdf.

[19] P. Tchounikine. Initier les élèves à la pensée informatique et à la programmation avec scratch. Research paper, 2016. http://lig-membres.imag.fr/tchounikine/PenseeInformatiqueEcole.html.

[20] Ikka Tuomi. The impact of artificial intelligence on learning, teaching, and education. Submitted file, 2018. http://publications.jrc.ec.europa.eu/repository/bitstream/JRC113226/jrc113226_jrcb4_the_impact_of_artificial_intelligence_on_learning_
final_2.pdf.

[21] Alan Turing. Computing machinery and intelligence. Mind, Oxford University Press, 59(236):33–35, 1950.

[22] The NESCO/Netexplo Advisory Board (UNAB). Human learning in the digital era. Report, 2019.  https://unesdoc.unesco.org/ark:/48223/pf0000367761.locale=en.

[23] UNESCO. Unesco and sustainable development goals. Policy document, 2015. http://en. unesco.org/sdgs.

[24] UNESCO. Unesco ICT competency framework for teachers. Report, 2018. https://unesdoc.unesco.org/ark:/48223/pf0000265721.

[25] Cédric Villani. Donner un sens à l’intelligence artificielle. Position document, 2018. https: //www.aiforhumanity.fr/pdfs/9782111457089_Rapport_Villani_accessible.pdf.

[26] Randi Williams, Hae Won Park, and Cynthia Breazeal. A is for artificial intelligence: The impact of artificial intelligence activities on young children’s perceptions of robots. In CHI Conference on Human Factors in Computing Systems Proceedings (CHI 2019), May 4–9, 2019, Glasgow, Scotland UK.ACM, New York, NY, USA. ACL, 2019. https://doi.org/10. 1145/3290605.3300677.

[27] Carolyn Wilson, Alton Grizzle, Ramon Tuazon, Kwame Akyempong, and Chi Kim Cheung. Media and information literacy curriculum for teachers. Report, 2014. https://unesdoc. unesco.org/ark:/48223/pf0000192971.locale=en.

[28] Jeannette M. Wing. Computational thinking. Communications of the ACM, 49(3):33–35, 2006.

[29] x5gon. Cross modal, cross cultural, cross lingual, cross domain, and cross site global oer network artificial intelligence and open educational resources. European project h2020, 2017. https://x5gon.org.

[30] Les échos. AI for business. Comment faire des entreprises françaises des championnes de l’IA ? Livre blanc, 2019. https://www.lesechos-events.fr/think-tank/ai-business/#fndtn-recommendations.

On 29 May 2018, the Government of Slovenia proposed to the Director-General the establishment of a Category 2 Centre on Artificial Intelligence (AI) under the auspices of UNESCO at Jožef Stefan Institute (JSI), Ljubljana, Slovenia.

The Knowledge 4 All Foundation was highly instrumental in achieving this success by supporting both sides.

Based thereon, UNESCO undertook the required feasibility study and assessed the proposed Centre’s scope, objectives, strategies and networking with other institutions from UNESCO’s vantage point.

The feasibility study also examined the available and promised human, material and financial resources for the Centre’s operations and sustainability. Further, it ascertained the commitments of both the Government of Slovenia and the Jožef Stefan Institute towards the proposed Category 2 Centre.

In order to allow full functional autonomy and as per the provisions set out in the revised integrated strategy on Category 2 Centre and institutes (37/C-18/PART_I), an International Research Centre on Artificial Intelligence (IRCAI) will be created by transferring the operations of the Centre for Knowledge Transfer in Information Technologies (CT3) within JSI.

JSI currently has more than 950 researchers, of which 180 cover areas within Artificial Intelligence, such as Machine Learning, Data-Mining, Text-Mining, Web-Mining, Multimedia Mining, Semantic Technologies, Social Network Analysis, Language Technologies, Natural Language Processing, Cross-lingual Technologies, Real-time Data Analysis, Data Visualization, Knowledge Management, Knowledge Reasoning, Inductive Logic Programming, Evolutionary Computation, Multistrategy learning and principles of multiple knowledge, among others.

IRCAI’s overall objective cover Research, Advocacy, Capacity Building and Dissemination of information about artificial intelligence and other advanced ICTs. The objectives of the Centre shall be to:

• Conduct theoretical and applied research in the field of artificial intelligence and advanced ICTs;
• Develop open solutions to help achieve sustainable development goals with specific focus on SDGs 4, 5, 8, 9, 10, 13, 16 and 17;
• Provide policy support to help member states address the technical, legal, social and ethical challenges at the intersection of technology and policy;
• Provide training for upstream and downstream capacity enhancement for artificial intelligence;
• Encourage multi stakeholder participation and decision making in addressing the challenges raised by artificial intelligence;
• Disseminate information and encourage literacy about artificial intelligence;
• Promote measures for removal of gender bias in the development and deployment of artificial intelligence;
• Facilitate north-north and north-south cooperation in the development of artificial intelligence with special emphasis on supporting the development of a vibrant artificial intelligence ecosystem in Africa.

IRCAI’s four objectives of research, capacity enhancement, advocacy and dissemination of knowledge of AI for SDGs aligns with UNESCO’s mandate of leveraging ICTs for sustainable development with a special emphasis on SDGs 4, 5, 8, 9, 10, 13, 16 and 17.

Knowledge 4 All Foundation played a pivotal role in the creation of the new UNESCO Recommendation in OER that can be implemented via our technology and can be used to empower all other stakeholders.

Preamble

The General Conference of the United Nations Educational, Scientific and Cultural Organization (UNESCO), meeting in Paris in 2019, at its 40th session,

Recalling that the Preamble of UNESCO’s Constitution affirms, “that the wide diffusion of culture, and the education of humanity for justice and liberty and peace are indispensable to the dignity of man and constitute a sacred duty which all the nations must fulfil in a spirit of mutual assistance and concern”,

Recognizing the important role of UNESCO in the field of information and communication technologies (ICT) and in the implementation of the relevant decisions in this area adopted by the General Conference of that Organization.

Further recalling Article I of UNESCO’s Constitution, which assigns to UNESCO among other purposes that of recommending “such international agreements as may be necessary to promote the free flow of ideas by word and image”,

Affirming the principles embodied in the Universal Declaration of Human Rights, that states all people have rights and fundamental freedoms that include the right to seek, receive and impart information and ideas through any media and regardless of frontiers (Article 19), as well as the right to education (Article 26), and the right to freely participate in the cultural life of the community, to enjoy the arts, and to share in scientific advancement and its benefits; and the right to the protection of the moral and material interests resulting from any scientific, literary, or artistic production of which one is the author (Article 27),

Also affirming the 2007 UN Declaration on the Rights of Indigenous Peoples, which recognizes the rights of indigenous peoples in formulating national legislation and implementing national policy,

Noting the 2006 Convention on the Rights of People with Disabilities (Article 24), which recognizes the rights of persons with disabilities to education and the principles contained in the Convention against Discrimination in Education (1960),

Referring to the resolutions of the General Conference of UNESCO with regard to the promotion of multilingualism and universal access to information in cyberspace,

Also referring to the 1997 UNESCO Recommendation concerning the Status of Higher-Education Teaching Personnel as well as the 1966 ILO/UNESCO Recommendation concerning the Status of Teachers which stresses that as part of academic and professional freedom teachers “should be given the essential role in the choice and adaptation of teaching material, the selection of textbooks, and the application of teaching methods”,

Reaffirming the importance of the United Nations 2030 Agenda for Sustainable Development, which underlines that the “spread of information and communications technology and global interconnectedness has great potential to accelerate human progress, to bridge the digital divide and to develop knowledge societies…” and of Goal 4 (SDG 4), which calls for the International community to “ensure inclusive and equitable quality education and promote lifelong opportunities for all”,

Acknowledging the 2003 World Summit on the Information Society, Declaration of Principles, committing “to build a people-centered, inclusive and development-oriented Information Society where everyone can create, access, utilize and share information and knowledge”,

Referring to the Education 2030 Framework for Action that lists a set of strategic approaches for the implementation of SDG 4, and which underlines increasing access must be accompanied by measures to improve the quality and relevance of education and learning, and in particular that ‘ Education institutions and programmes should be adequately and equitably resourced.. and books other learning materials, open educational resources and technology that are non-discriminatory, learning conducive, learner friendly, context specific, cost effective and available to all learners – children, youth and adults.’

Recognizing that the development of ICTs provides opportunities to improve the free flow of ideas by word, sound and image but also presents challenges for ensuring the participation of all in Knowledge Societies,

Recognizing that quality basic education, as well as media and information literacy are prerequisites to access and benefit from ICTs,

Also recognizing that, in building inclusive Knowledge Societies, Open Educational Resources (OER) can support quality education that is equitable, inclusive, open and participatory as well as enhance academic freedom and professional autonomy of teachers by widening the scope of materials available for teaching and learning,

Considering the 2007 Cape Town Open Education Declaration, the 2009 Dakar Declaration on Open Educational Resources, the 2012 Paris OER Declaration, the Millennium Declaration, the 2000 Dakar Framework for Action, and the International Covenant on Economic, Social and Cultural Rights (Article 13.1), which all recognize “the right of everyone to education”,

Building on the Ljubljana OER Action Plan 2017 to mainstream OER to help all Member States to create inclusive Knowledge Societies and achieve the 2030 Sustainable Development Agenda, namely SDG 4 (quality education), SDG 5 (Gender equality), SDG 9 (Infrastructure), SDG 10 (Reduced inequalities within and across countries), SDG 16 (Peace, justice and strong institutions) and SDG 17 (Partnerships for the goals), thereto:

1. Adopts the present Recommendation on Open Educational Resources (OER);
2. Recommends that Member States apply the following provisions by taking appropriate steps, including whatever legislative or other measures may be required, in conformity with the constitutional practice and governing structures of each State, to give effect within their territories to the principles of the Recommendation;
3. Also recommends that Member States bring the Recommendation to the attention of the authorities and bodies responsible for learning and education and consult relevant stakeholders concerned with learning and education;
4. Further recommends that Member States report to UNESCO, at such dates and in such manner as shall be determined, on the action taken in pursuance of this Recommendation.

Definition and Scope

1. Open Educational Resources (OER) are teaching, learning and research materials in any medium that may be composed of copyrightable materials released under an open license, materials not protected by copyright, materials for which copyright protection has expired, or a combination of the foregoing.
2. Open license refers to a copyright license that respects the intellectual property rights of the copyright owner and provides limited permissions granting the public the rights to access, use, adapt, and redistribute educational materials.
3. Information and communication technologies (ICT) provide great potential for effective, equitable and inclusive access to OER and their use, adaptation and redistribution. They can open possibilities for OER to be accessible anytime and anywhere for everyone including individuals with disabilities and individuals coming from marginalized or disadvantaged groups. They can help meet the needs of individual learners and effectively promote gender equality and incentivize innovate pedagogical, didactical and methodological approaches.
4. Stakeholders in this Recommendation include: educators, learners, governmental bodies, parents educational institutions, education support personnel, teacher trainers, educational policy makers, cultural institutions (such as libraries, archives and museums) and their users, technical infrastructure providers, researchers, research institutions, civil society organizations (including professional and student associations), publishers, the public and private sectors, intergovernmental organizations, copyright holders and authors, media and broadcasting groups and funding bodies.

Aims and Objectives

5. One key prerequisite to achieve SDG 4 is sustained investment and educational actions by governments and other key education stakeholders, as appropriate, in the creation, curation, regular updating, ensuring inclusive and equitable access, and effective use of high quality materials and programmes of study.
6. As is articulated in the 2007 Cape Town Open Education Declaration and the 2012 Paris OER Declaration, the application of open licenses to educational materials introduces significant opportunities for more cost-effective creation, access, use, adaptation, redistribution, curation, and quality assurance of those materials, including, but not limited to translation, adaptation to different learning and cultural contexts, development of gender-sensitive materials, and the creation of alternative and accessible formats of materials for learners with special educational needs.
7. In addition, the judicious application of OER in combination with appropriate pedagogical methodologies can support a broad range of innovative pedagogical options to engage both educators and learners to become more active participants in educational processes and creators of content as members of diverse and inclusive Knowledge Societies.
8. Furthermore, regional and global collaboration and advocacy in the creation, access, use, adaptation, redistribution and evaluation of OER can enable governments to optimise their own investments in educational content creation, as well as IT infrastructure and curation, in ways that will enable them to meet their defined national educational policy priorities more cost-effectively and sustainably.

9. Noting these potential benefits, the objectives and areas of action of this OER Recommendation are as follows:

(i) Capacity building: developing the capacity of all key education stakeholders to create, access, use, adapt, and redistribute OER, as well as to use and apply open licenses in a manner consistent with national copyright legislation and international obligations;

(ii) Developing supportive policy: encouraging governments, and education authorities and institutions to adopt regulatory frameworks to support open licensing of publicly funded educational materials, develop strategies to enable use and adaptation of OER in support of high quality, inclusive education and lifelong learning for all, and adopt integrated mechanisms to recognize the learning outcomes of OER-based programmes of study;

(iii) Effective, inclusive and equitable access to quality OER: supporting the adoption of strategies and programmes including through relevant technology solutions that ensure OER in any medium are shared in open formats and standards to maximize equitable access, co-creation, curation, and search ability, including for those from vulnerable groups and persons with disabilities;

(iv) Nurturing the creation of sustainability models for OER: supporting and encouraging the creation of sustainability models for OER at national and institutional levels, and the planning and pilot test of new sustainable forms of education and learning;

(v) Facilitating international cooperation: supporting international cooperation between stakeholders to minimize unnecessary duplication in OER development investments and to develop a global pool of culturally diverse, locally relevant, gender-sensitive, accessible, educational materials in multiple languages.

Areas of Action

10. This Recommendation addresses five objectives: (i) Building capacity of stakeholders to create, access, use, adapt and redistribute OER; (ii) Developing supportive policy; (iii) Encouraging inclusive and equitable quality OER; (iv) Nurturing the creation of sustainability models for OER; and (v) Facilitating international cooperation.

Building Capacity of Stakeholders to create, access, use, adapt and redistribute OER

11. Member States are recommended to strategically plan and support OER capacity building and awareness raising at the institutional and national levels, targeting all education sectors and levels. Member States are encouraged to consider the following:

(a) building awareness among relevant stakeholder communities on how OER can increase access to educational resources, improve learning outcomes, maximise the impact of public funding, and empower educators and learners to become co-creators of knowledge;

(b) providing systematic and continuous capacity building (in-service and pre-service) on how to create, access, make available, use, adapt, and redistribute OER as an integral part of training programmes at all levels of education. This should include improving capacity of public authorities, policy makers, quality development and assurance professionals to understand OER and support their integration into teaching and learning;

(c) raising awareness concerning exceptions and limitations for the use of copyrighted works for educational and research purposes. This should be enacted to facilitate the integration of a wide range of works in OER, recognizing that the fulfilment of educational goals as well as the development of OER requires engagement with existing copyright protected works.

(d) leveraging open licensed tools, platforms and standards to help ensure OER can be easily found, accessed, used, adapted and redistributed in a safe, secure and privacy protected mode. This could include free and open source authoring tools, libraries and other repositories and search engines, systems for long term preservation and frontier technologies for automatic OER processing such as artificial intelligence methods and tools; and

(e) making available easily accessible resources that provide information and assistance to all OER stakeholders on OER related topics including copyright and open licensing of educational material.

Developing supportive policy

12. Member States, according to their specific conditions, governing structures and constitutional provisions, should develop or encourage policy environments, including those at the institutional level, that are supportive of effective OER practices. Through a transparent participatory process that includes dialogue with stakeholders, Member States are encouraged to consider the following:

(a) developing and implementing policies and/or regulatory frameworks which encourage that educational resources developed with public funds be openly licensed or dedicated to the public domain as appropriate, and financial and human resources coordinated for the implementation of the policies;

(b) encouraging and supporting institutions to develop or update legal or policy frameworks to stimulate the creation, access, use, adaptation and redistribution of quality OER by educators and learners; and to develop and integrate quality assurance mechanism for OER into the existing quality assurance strategies for teaching and learning materials;

(c) developing mechanisms to support and incentivize all stakeholders to publish source files and accessible OER using standard open file formats in public repositories;

(d) aligning OER policies with other open policies and guiding principles such as those for Open Access, Open Data, Open Pedagogy, Open Source Software and Open Science; and

(e) adjusting or reforming the curriculum and assessment in accordance with the needs of the use of OER and to motivate the active use, creation, and sharing of OER by teachers and students; and recognizing the learning outcomes of OER-based programmes of study;

Encourage inclusive and equitable OER

13. Member States are encouraged to support the creation, access, use, adaptation and redistribution of inclusive and equitable quality OER for all stakeholders. These would include those learners in formal and non-formal education contexts irrespective of age, gender, physical ability, socio-economic status, as well as those who live in remote areas (including nomadic populations), internally and forcibly displaced persons, migrants and refugees. In all instances, gender equality should be ensured, and particular attention paid to equity and inclusion for learners who are especially disadvantaged due to multiple and intersecting forms of discrimination. Member States are recommended to consider the following:

(a) ensuring access to OER that most suitably meet both the needs and material circumstances of target learners and the educational objectives of the courses or subjects for which they are being provided. This would include offline (including printed) modalities for accessing resources where appropriate;

(b) supporting OER stakeholders to develop gender-sensitive, culturally and linguistically relevant OER, and to create local language OER, particularly in indigenous languages which are less used, under- resourced and endangered;

(c) ensuring that the principle of gender equality, non-discrimination, accessibility and inclusiveness is reflected in strategies and programmes for creating, accessing, using, adapting, and redistributing OER;

(d) incentivising public and private investments in IT infrastructure and broadband, to provide increased access to OER, particularly for low-income, rural and remote communities; and

(e) developing and adapting existing evidence-based standards, benchmarks and related criteria for the quality assurance of OER, as appropriate, which emphasize reviewing educational resources (both openly licensed and not openly licensed) under regular quality assurance mechanisms.

Nurturing the creation of sustainability models for OER

14. Member States, according to their specific conditions, governing structures and constitutional provisions, are recommended to support and encourage the development of comprehensive, inclusive and integrated OER sustainability models. Member States are encouraged to consider the following:

(a) reviewing current provisions, procurement policies and regulations to expand and simplify the process of procuring quality goods and services to prioritize the creation, ownership, translation, adaptation, curation, and sharing of OER, where appropriate, as well as to develop the capacity of all OER stakeholders to participate in these activities;

(b) catalysing sustainability models through traditional funding sources but also non-traditional reciprocity based revenue generation such as donations, memberships, pay what you want, and crowdfunding that may provide revenues and sustainability to OER provision;

(c) promoting and raising awareness of other value added models using OER across institutions and countries where the focus is on participation, co-creation, generating value collectively, community partnerships, spurring innovation, and bringing people together for a common cause; and

(d) enacting regulatory frameworks that support the development of OER products and services that align with national standards as well as the interest and values of the OER stakeholders.

Facilitating international cooperation

15. To promote the development and use of OER, Member States should facilitate international cooperation among all relevant stakeholders, whether on a bilateral or multilateral basis. Member States are encouraged to consider the following:

(a) promoting and stimulating cross-border collaboration and alliances on OER projects and programmes, leveraging existing transnational, regional and global collaboration mechanisms and organizations. This should include joining efforts on collaborative development and use of OER as well as capacity building, communities of practice, joint research on OER, and mutual cooperative assistance between all countries regardless of their state of development;

(b) exploring methods to establish regional and international funding mechanisms for implementing and strengthening OER as well as to understand mechanisms that can support international, national, and regional efforts;

(c) supporting the creation and maintenance of effective peer networks that share OER, based on areas such as subject matter, language, institutions, regions, and level of education, at local, regional and global levels; and

(d) incorporating, where appropriate, specific clauses relating to OER in international agreements concerned with cooperation in the fields of education.

Monitoring

16. Member States should, according to their specific conditions, governing structures and constitutional provisions, evaluate policies and programmes related to OER using a combination of quantitative and qualitative approaches, as appropriate. Member States are encouraged to consider the following:

(a) deploying appropriate research programmes, tools and indicators to measure the effectiveness and efficiency of OER policies and incentives against defined objectives, including specific targets for disadvantaged and vulnerable groups;

(b) collecting, presenting, and disseminating progress, good practices, innovations and research reports on OER and its implications with the support of UNESCO and international open education communities; and

(c) developing strategies to monitor and evaluate the educational effectiveness and long-term financial efficiency of OER, which include participation of all relevant stakeholders. Such strategies could focus on improving learning processes and strengthening the connections between findings, decision-making, transparency, and accountability to ensure the best educational outcomes. This would include support to the development of an evidence base on the impact of OER on education and learning.

Abstract

To create an automatic data annotation tool and ground truth dataset for malaria diagnosis using deep learning. The ground truth dataset and the tool will streamline the development of AI tools for pathology diagnosis.

Introduction

Technology is transforming how health care is delivered in Africa, providing more people especially in limited resource setting areas and around the world access to better care. Likewise, easier access to data supports both doctors and policymakers in making better-informed decisions about how to continue to improve the health care system. However, the existing traditional methods especially for disease diagnosis have limitations such as expensive equipment, need of experts and time consuming for a single diagnosis. This becomes impractical in areas with high disease burden such as sub-Saharan regions. In this project we focus on improvement of malaria diagnosis.We choose malaria because it is a life threatening disease dominant in developing countries. According to WHO In 2017, nearly half of the world’s population was at risk of malaria with more than 90 countries reporting malaria cases and Africa was home to 435,000 death. Also they report that malaria kills a child every 2 minutes. Nevertheless, prompt diagnosis and treatment can reduce such death.

In the area of Artificial Intelligence (AI), several techniques have been adopted to create malaria diagnosis tools that are fast, accurate and requires less experts. Deep convolutional networks as one of AI techniques, has been used for detection of malaria parasites (Sanchez Sanchez, 2015). Concerning the sensitivity of health, AI tools dealing with health issues such as diagnosis usually require large amounts of data in order to achieve high accuracy for its applicability. However, in the context of developing countries there is a shortage of such data for research and developing such tools. Henceforth, there is a necessity of creation of dataset for research and development of pathology diagnosis tool such as for malaria.

Rationale

One of the major problems that hinders development of AI and its applicability in developing countries include lack of data. This is evident in limited access to the available data from both government and non-governmental organization. In addition to that, data may be available but still lacks the necessary quality in terms of pixels, labels that is required for development of AI tools. Lastly, in some scenarios such as agriculture and health there is no data to be used for training, testing and validation of AI tools. For these reasons, it becomes difficult and takes a longer time to create a comprehensive dataset. These problems regarding dataset, particularly in the health sector, cause a significant setback to the AI tools development which is a potential technology in solving problems in our health sector. Therefore, there is a need of  coming up with a tool for improving the entire process of acquiring dataset.

Main Objective

The aim of this project is to create AI tool that will be used to effectively create ground truth dataset for malaria diagnosis using deep learning. Specific Objectives:

• To capture microscopic images of malaria parasitized and uninfected stained blood smear
  sample using a smartphone.
• To develop automatic annotation tool for the captured images by integrating an open
  source annotation tool and object detection model.
• To verify the effectiveness of the automatic annotation tool.

Abstract

To initiate a research roadmap for the preservation of indigenous languages through the means of collecting, categorizing and archiving of translation and voice synthesis to perform the automatic translation in official and indigenous languages.

Objectives

Build, Curate and Explore a massive dataset of public content in indigenous languages. The objective is to identify and enumerate data sources for retrieving content in a indigenous language, creating an open archive that can be leveraged in a variety of activities, including for training translation models to promote national languages, or for building vocal synthesizers to help distribute news content to illiterate citizens.

Initiate a research roadmap on translation and voice synthesis to promote indigenous languages through content sharing Preserving indigenous languages is a challenging endeavor which require first closing the information gap that may exist between official (mainly colonial) languages and indigenous languages. For  example, news content are abundant in official languages, while rural areas are provided with brittle summaries in indigenous languages. Artificial intelligence can help in closing the gap through automatic translation of texts and voice synthesis (to account for illiteracy). The project will initiate a state-of-the-art survey of available and missing components in the context towards realizing this endeavor.

Long-term vision

The long-term vision of the preservation project is to ensure that indigenous languages, hence the indigenous cultures, are sustained. To that end, the project investigates:

• the means to systematize the collection and archiving the contents. This will ensure that all data are openly made available in readily processible formats and in a unique repository endpoint
• the opportunity to perform automatic translation to ensure a back-and-forth exchange of viewpoints in official and indigenous languages
• the democratization of information by the elite to the rural citizens who only speak indigenous languages.

This last point is the ultimate goal towards preserving indigenous languages by ensuring that the information gap is closed, thus realizing one objective of open data, which is to increase democratic participation via information.