Keynote Speakers




Francesca Rossi

When: September 17th h 9:30 – 10:00

IBM AI Ethics Global Leader and Distinguished Research Staff Member at IBM Research.
Previously, Professor of Computer Science at the University of Padova, Italy.

Her research interests focus on artificial intelligence, specifically they include constraint reasoning, preferences, multi-agent systems, computational social choice, and collective decision making. She is also interested in ethical issues in the development and behaviour of AI systems, in particular for decision support systems for group decision making. She has published over 190 scientific articles in journals and conference proceedings, and as book chapters. She has co-authored a book and she has edited 17 volumes, between conference proceedings, collections of contributions, special issues of journals, and a handbook.

She is a fellow of both the worldwide association of AI (AAAI) and of the European one (EurAI).
She has been president of IJCAI (International Joint Conference on AI), an executive councillor of AAAI,
and the Editor in Chief of the Journal of AI Research. She is a member of the scientific advisory board of the Future of Life Institute (Cambridge, USA) and a deputy director of the Leverhulme Centre for the Future of Intelligence (Cambridge, UK). She is in the executive committee of the IEEE global initiative on ethical considerations on the development of autonomous and intelligent systems and she is a member of the board of directors of the Partnership on AI, where she represents IBM as one of the founding partners. She is a member of the European Commission High Level Expert Group on AI and the general chair of the AAAI 2020 conference.


AI is going to bring huge benefits in terms of scientific progress, human well-being, economic value, and the possibility of finding solutions to major social and environmental problems. However, such a powerful technology also raises some concerns, related for example to the black-box nature of some AI approaches, the possible discriminatory decisions that AI algorithms may recommend, the accountability and responsibility when an AI system is involved in an undesirable outcome, and the usage of data. Without adequate answers to these concerns, many will not trust AI, and therefore will not fully adopt it nor get its positive impact. In this talk I will present the main issues around AI ethics and describe some of the proposed solutions.


Sihem Amer-Yahia

When: September 18th h 9:00 – 9:30

Sihem is a CNRS Research Director. Her interests are at the intersection of large-scale data management and social data exploration. Sihem held positions at QCRI, Yahoo! Research and AT&T Labs. She served on the SIGMOD Executive Board and the VLDB Endowment. She is Editor-in-Chief of the VLDB Journal and associate editor of TDS. She served as PC chair for PVLDB 2018, WWW 2019 Workshops and SIGMOD 2020 Demonstrations.  Find more about her on



Abstract:  Human Factors in Data Science

Data Science (DS) has been mainly concerned with developing libraries and stacks to ingest data anc create value. Humans have been playing a major role in different layers of DS. This talk argues that the DS lifecycle can only be realized by looping in humans in an efficient and safe fashion. I will discuss how human factors affect algorithm design in DS. In particular, I will illustrate adaptive optimization to account for motivation, and algorithmic fairness on virtual marketplaces.


Danielle (Sparkie) VanDyke

When: September 18th h 9:30 – 10:00

Danielle has been at Google for 13 years, both in the Mountain View, California headquarters and in London, UK. In her current role as a Software Reliability Engineering manager, she leads teams working on the infrastructure & tooling to make Google’s mobile applications stronger, faster, and more reliable.

Over the years at Google, she has been on more than 10 different teams in various roles including manager, team lead, software developer, and quality assurance engineer. She has worked with several engineer teams that joined Google through an acquisition, as well guided a couple of teams through relocation or shutdown.


Abstract: TDB