Summary

In artificial intelligence, algorithmic bias is a real issue. It underlies the ingestion of subjective data by the machine learning model.

These biases tend to carry risks of discrimination for certain populations, reflecting those that we deplore in our society with regard to skin colour, gender, age or nationality.

So how can we recognise bias in artificial intelligence and how can we avoid it?

Moderator

Jenny Benois-Pineau is a professor of Computer Science at the University Bordeaux. Her topics of interest include image/multimedia, artificial intelligence in multimedia and healthcare. She is the author and co-author of more than 200 papers in international journals, conference proceedings, books and book chapters. She has tutored and co-tutored 28 PhD students. She is area editor of SPIC, ACM MTAP, senior associated editor JEI SPIE journals. She has served in numerous program committees in international conferences: ACM MM, ICMR, CIVR, CBMI, IPTA, MMM, and organized WS and a video track at ACM MM, ICPR… She has been coordinator or leading researcher in EU – funded and French national research projects. She has been an expert for EU calls in digital content and health and French National Agency of Research (ANR). She is chair of International Relations of School of Sciences and Technologies of the University of Bordeaux, comprising circa 9500 students. She is a member of IEEE TC IVMSP. She has Knight of Academic Palms grade.

Panelists

• Rupayan Malick, IMS Université de Bordeaux
• Renaud Peteri, La Rochelle University
• Lusine Abrahamyan, Vrije Universiteit Brussel