Organised by

UNESCO Communication and Information (CI) Sector

Internet Society (ISOC)

In order to reflect on the significant impact of emerging and smart technologies, including Artificial Intelligence (AI) and the Internet of Things (IoT) on UNESCO’s core mandate to promote the free flow of ideas by word and image and build inclusive knowledge societies, UNESCO’s Communication and Information (CI) Sector, in partnership with Internet Society (ISOC), will be organizing an open discussion on “Harnessing AI to Foster Knowledge Societies and Good Governance” following the Internet Governance Forum (IGF) in November 2018 in Paris, France.

This event will be dedicated to unpacking the technological, ethical, political, social, and legal implications of the development and application of AI. The event is conceived as an open forum for multi-stakeholder dialogue on these issues to explore policies, practices, measures and mechanisms needed for harnessing AI to advance knowledge societies and good governance.


08:30‑09:00 Registration of Participants and Coffee
09:00‑09:30 Opening Remarks and introductory keynote(s):
  • Sylvestre Ledru, Head of the French branch, Mozilla corporation
  • Raul Echeberria, Vice President, Global Engagement, Internet Society
  • Moez Chakchouk, Assistant Director General, Communication andInformation Sector, UNESCO
09:30‑10:00 Artificial Intelligence – What is it all about?
  • Lynn St. Amour, Chair, Internet Governance Forum’s Multistakeholder Advisory Group
  • Dr. Ansgar Koene, Professor, University of Nottingham, School of Computer Science - Ethics, Rights, Privacy on the internet

What is being done at the international level to consider AI in sustainable development and good governance?

  • Dr. Cherif Diallo, Director of information technologies and ICT, Ministry of Communications, Telecommunications, Postal Services and the Digital Economy, Senegal
  • H. E. Mr Federico Salas Lotfe Ambassador, Permanent Delegate of Mexico to UNESCO
10:00-10:30 Coffee and networking Break
10:30-11:45 Session 1: AI Advocacy

Chair: Gasper Hrastelj, Deputy Secretary-General, Slovenian National Commission for UNESCO


  • Paula Forteza, French Member of Parliament
  • Davor Orlic, Senior Project Manager, Jozef Stefan Institute, Chief Operating Officer, K4A foundation
  • Brigitte Lasry, Head of University Network, Netexplo
  • Dr. Etienne Parizot, founding member, JamaisSansElles, Professor, Université Paris Diderot – Paris-VII
  • Lars-Erik Forsberg, Deputy Head of Unit, Policy Outreach & International Affairs, DG CNECT, European Commission
  • Jean-Pierre Evain, Principal Project Manager, Technology & innovation, EBU

Rapporteur: Odile Ambry, Internet Society France

11:45-13:00 Session 2: Human rights and AI

Chair: Dr. Eileen Donahoe, Executive Director, Stanford Global Digital Policy Incubator


  • Mohamed Farahat, Lawyer and coordinator of the legal aid project of the Egyptian foundation for refugee rights
  • Michael J. Oghia, Communications manager, Global Forum for Media Development (GFMD)
  • Julie Owono, Executive Director, Internet Sans Frontieres
  • Katie Evans, PhD researcher on the ethics of AI
  • Dr. François Bertrand, Deputy Director-General, Polytechnique Montréal
  • Samhir Vasdev, Advisor for Digital Development, IREX

Rapporteur: Agustina Callegari, Internet Society

13:00‑14:00 Lunch
14:00‑14:30 Keynote and Q&A session: Bruno Lanvin, Executive Director of Global Indices,
15:00‑15:30 Session 3: Translating ROAM principles into AI governance and practice through an open and inclusive multi-stakeholder approach

Chair: Guy Berger, Director for Freedom of Expression and Media Development, UNESCO


  • David Souter, Research Associate, Oxford Internet Institute, UNESCO commissioned author on Internet Universality indicatorsMatthias Spielkamp, Founder and Executive Director, AlgorithmWatch
  • Dr. Małgorzata Bielenia, Assistant Professor, University of Business and
    Administration, Gdynia, Poland
  • Paul Massen, Chief, Country Support, Open Government Partnership
  • Dr. Alexandre Barbosa, Head of the Center of Studies for Information and Communications Technologies, CETIC.BR

Rapporteur: Paula Real, Internet Society

16:15‑16:45 Keynote and Q&A session: Yaniv Gelnik, Chief Strategist and Global Business
Development Lead, Zipline
15:45‑17:00 Session 4: Understanding Openness and inclusiveness in the age of advanced ICTs

Chair: Indrajit Banerjee, Director, Knowledge Societies Division, UNESCO


  • Dr. Frédéric Bouchard, Professor, Dean of the Faculty of Arts and Science, Université de Montréal
  • Dr. Maria Fasli, Professor, University of Exete, UNESCO chair on Data Science
  • Bruno Lanvin, Executive Director of Global Indices, INSEAD
  • Dr. John Shawe-Taylor, Professor, University College London, K4A Foundation, UNESCO Chair in Artificial Intelligence
  • Daewon Kim, Director of Public Affairs, Corporate Ethics, Kakao
  • Dr. Hugo Cyr, Dean of the Faculty of Political science and Law, UQAM
  • Dr. Marie-Hélène Parizeau, Professor, Université Laval, COMEST member

Rapporteur: Julien Rossi, Internet Society France, Université Rennes 2

17:00‑18:00 Debriefing from Rapporteurs on each session 1-4
Outcomes and proposals
  • Moderators and Rapporteurs
18:30‑20:00 Cocktail

This symposium addresses a topic that has spurred vigorous scientific debate of late in the fields of neuroscience and machine learning: causality in time-series data. In neuroscience, causal inference in brain signal activity (EEG, MEG, fMRI, etc.) is challenged by relatively rough prior knowledge of brain connectivity and by sensor limitations (mixing of sources). On the machine learning side, as the Causality workshop last year’s NIPS conference has evidenced for static (non-time series) data, there are issues of whether or not graphical models (directed acyclic graphs) pioneered by Judea Pearl, Peter Spirtes, and others can reliably provide a cornerstone of causal inference, whereas in neuroscience there are issues of whether Granger type causality inference is appropriate given the source mixing problem, traditionally addressed by ICA methods. Further topics, yet to be fully explored, are non-linearity, non-Gaussianity and full causal graph inference in high-dimensional time series data. Many ideas in causality research have been developed by and are of direct interest and relevance to researchers from fields beyond ML and neuroscience: economics (i.e. the Nobel Prize winning work of the late Clive Granger, which we will pay tribute to), process and controls engineering, sociology, etc. Despite the long-standing challenges of time-series causality, both theoretical and computational, the recent emergence of cornerstone developments and efficient computational learning methods all point to the likely growth of activity in this seminal topic.

Along with the stimulating discussion of recent research on time-series causality, we will present and highlight time-series datasets added to the Causality Workbench, which have grown out of last year’s Causality challenge and NIPS workshop, some of which are neuroscience related.

Programme Committee

  • Luiz Baccala (Escola Politecnica da Universidade de Sao Paulo, Brazil)
  • Katarina Blinowska (University of Warsaw, Poland)
  • Alessio Moneta (Max Planck Institute of Economics, Germany)
  • Mischa Rosenblum (Potsdam University, Germany)
  • Bjoern Schelter (Freiburg Center for Data Analysis and Modeling, Germany)
  • Pedro Valdes-Sosa (Neurosciences Center of Cuba)

Nowadays, there are massive amounts of heterogeneous electronic information available on the Web. People with disabilities, among other groups potentially in uenced by the digital gap, face great barriers when trying to access information. Sometimes their disability makes their interaction the ICT environment (eg., computers, mobile phones, multimedia players and other hardware devices) more dicult. Furthermore, the contents are delivered in such formats that cannot be accessed by people with disability and the elderly. The challenge for their complete integration in information society has to be analyzed from di erent technology approaches.

Recent developments in Machine Learning are improving the way people with disabilities access to digital information resources. From the hardware perspective, Machine Learning can be a core part for the correct design of accessible interaction systems of such users with computers (such as BCI). From the contents perspective, Machine Learning can provide tools to adapt contents (for instance changing the modality in which it is accessed) to users with special needs. From the users' perspective, Machine Learning can help constructing a good user modeling, as well as the particular context in which the information is accessed.

Time series forecasting is a challenge in many fields. In finance, one forecasts stock exchange courses or stock market indices; data processing specialists forecast the flow of information on their networks; producers of electricity forecast the load of the following day. The common point to their problems is the following: how can one analyze and use the past to predict the future? Many techniques exist including linear methods such as ARX or ARMA, and nonlinear ones such as the ones used in the area of machine learning.

In general, these methods try to build a model of the process that is to be predicted. The model is then used on the last values of the series to predict future ones. The common difficulty to all methods is the determination of sufficient and necessary information for a good prediction. If the information is insufficient, the forecasting will be poor. On the contrary, if information is useless or redundant, modeling will be difficult or even skewed.

In parallel with this determination, a suitable prediction model has to be selected. In order to compare different prediction methods several competitions have been organized, for example, the Santa Fe Competition, the CATS Benchmark Competition and the ESTSP’07 Competition.

After the competitions, their results have been published and the time series have become widely used benchmarks. The goal of these competitions is the prediction of the subsequent values of a given time series (3–100 values to predict). Unfortunately, the long-term prediction of time series is a very difficult task.

Furthermore, after the publication of results, the real values that had to be predicted are also published. Thereafter, it becomes more difficult to trust in new results that are published: knowing the results of a challenge may lead, even unconsciously, to bias the selection of model; some speak about ‘‘data snooping’’. It becomes therefore more difficult to assess newly developed methods, and new competitions have to be organized.

This special issue is based on extended version of papers presented at the joined ESTSP’08 (European Symposium on Time Series Prediction) and AKRR’08 (Adaptive Knowledge Representation and Reasoning)  conferences. This shared event took place in Porvoo, Finland, from 17th to 19th of September, 2008. The goal of joining these conferences was to create an interdisciplinary forum for researchers who may widen their scope of attention beyond the usual scope of research. The  cross-fertilization took place, for instance, by offering the attendees shared keynote talks. Prof. Marie Cottrell (Paris University 1) gave a talk on data analysis using Self-Organizing Maps. Prof. Jose  Prı́ncipe (University of Florida) described information theoretic learning and kernel methods.

Dr. Harri Valpola (Helsinki University of Technology) explained how to extract abstract concepts from raw data using statistical machine learning methods. One specific shared theme of interest was anticipation, i.e., how an agent makes decisions based on predictions, expectations, or beliefs about the future. Anticipation is an important concept when complex natural cognitive systems are considered.