When AI Is Wrong: Addressing Liability Challenges in Women’s Healthcare

Abstract

Healthcare professionals can leverage Artificial intelligence (AI) to provide better care for theirpatients. However, it is also necessary to consider that AI algorithms operate according to historicaldiagnostic data, which often include evidence gathered from men. The biases of prior practices andthe perpetuation of exclusionary processes toward women can lead to inaccurate medical deci-sions. The ramifications of such errors show that the incorrect use of AI raises several criticalquestions regarding who should be responsible for potential incidents. This study aims to providean analysis of the role of AI in affecting women’s healthcare and an overview of the liabilityimplications caused by AI mistakes. Finally, this work presents a framework for algorithmic auditingto ensure that AI data are collected and stored according to secure, legal, and fair practices.