Jenny Benois Pineau leads the "Explainable Deep learning/AI" workshop at the IEEE ICPR '2021 IAPR conference. In connection with LABRI's emerging project Explanable AI, the aim is to propose methods to "understand" and "explain" how these systems produce their decisions.

AI systems may produce errors, can exhibit overt or subtle bias, may be sensitive to noise in the data, and often lack technical and judicial transparency and explainability. These shortcomings raise many ethical and policy concerns that impede wider adoption of this potentially very beneficial technology. In various Pattern Recognition and AI application domains such as health, ecology, autonomous driving cars, security, culture it is mandatory to understand how the predictions are correlated with the information perception and decision making by the experts and impact society and business.. The goals of this full day workshop are to bring together research community which is working on the question of improving explainability of AI and Pattern Recognition algorithms and systems. The Workshop is a part of ICPR'2020 and supported by research project XAI-LABRI.
More informations, here.