Explainability for identification of vulnerable groups in machine learning models
This work addresses an ethical issue in machine learning fairness by focusing on vulnerable groups, offering incremental improvements over existing fairness and explainability methods.
The paper tackles the problem of identifying whether machine learning models detect vulnerable groups, which are context-dependent unlike protected groups, and proposes new analytical approaches to address this gap.
If a prediction model identifies vulnerable individuals or groups, the use of that model may become an ethical issue. But can we know that this is what a model does? Machine learning fairness as a field is focused on the just treatment of individuals and groups under information processing with machine learning methods. While considerable attention has been given to mitigating discrimination of protected groups, vulnerable groups have not received the same attention. Unlike protected groups, which can be regarded as always vulnerable, a vulnerable group may be vulnerable in one context but not in another. This raises new challenges on how and when to protect vulnerable individuals and groups under machine learning. Methods from explainable artificial intelligence (XAI), in contrast, do consider more contextual issues and are concerned with answering the question "why was this decision made?". Neither existing fairness nor existing explainability methods allow us to ascertain if a prediction model identifies vulnerability. We discuss this problem and propose approaches for analysing prediction models in this respect.