Astrid Bertrand
Télécom Paris, Institut Polytechnique de Paris, Paris, France
Artificial intelligence (AI) has the potential to bring significant benefits to highly regulated industries such as healthcare or banking. Adoption, however, remains low. AI's entry into complex socio-techno-legal systems raises issues of transparency, specifically for regulators. However, the perspective of supervisors, regulators who monitor compliance with applicable financial regulations, has rarely been studied. This paper focuses on understanding the needs of supervisors in anti-money laundering (AML) to better inform the design of AI justifications and explanations in highly regulated fields. Through scenario-based workshops with 13 supervisors and 6 banking professionals, we outline the auditing practices and socio-technical context of the supervisor. By combining the workshops’ insights with an analysis of compliance requirements, we identify the AML obligations that conflict with AI opacity. We then formulate seven needs that supervisors have for model justifiability. We discuss the role of explanations as reliable evidence on which to base justifications.
doi.org/10.1145/3613904.3642326
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2024.acm.org/)