Confiance déplacée dans l'IA : le paradoxe de l'explication et l'approche centrée sur l'homme. Une caractérisation des défis cognitifs pour faire confiance de manière appropriée aux décisions algorithmiques et applications dans le secteur financier
Astrid Bertrand · theses.fr (ABES) · 2024
As AI is becoming more widespread in our everyday lives, concerns have been raised about comprehending how these opaque structures operate. In response, the research field of explainability (XAI) has developed considerably in recent years. However, little work has studied regulators' need for explainability or considered effects of explanations on users in light of legal requirements for explanations. This thesis focuses on understanding the role of AI explanations to enable regulatory compliance of AI-enhanced systems in financial applications. The first part reviews the challenge of taking into account human cognitive biases in the explanations of AI systems. The analysis provides several directions to better align explainability solutions with people's cognitive processes, including designing more interactive explanations. It then presents a taxonomy of the different ways to interact with explainability solutions. The second part focuses on specific financial contexts. One study takes place in the domain of online recommender systems for life insurance contracts. The study highlights that feature based explanations do not significantly improve non expert users' understanding of the recommendation, nor lead to more appropriate reliance compared to having no explanation at all. Another study analyzes the needs of regulators for explainability in anti-money laundering and financing of terrorism. It finds that supervisors need explanations to establish the reprehensibility of sampled failure cases, or to verify and challenge banks' correct understanding of the AI.