MACAIF: Machine Learning Auditing for Clinical AI Fairness

Artificial intelligence in the form of machine learning algorithms is driving the latest industrial revolution, leading to disruptive changes in the ways we communicate, interact, design, collect information, and express ourselves. While these changes offer new possibilities for our societies, they may also introduce biases that can lead to unfair decisions. This issue is particularly critical in the context of medical diagnosis, as bias can jeopardize patient treatment and health. To mitigate these biases, it is essential to such biases and involve all relevant stakeholders in the design of fair machine learning algorithms. In this context, the MACAIF project aims to develop user-centred interfaces that allow stakeholders, including doctors, to challenge the fairness of machine learning algorithms based on demographics, such as gender or race. Our project proposes a methodology to engage with stakeholders and incorporate their concerns during the design of a dashboard based on MLighter - an adversarial tool which is applied to identify fairness-related issues in machine learning models.