Explainability for identification of vulnerable groups in machine learning models

• URL: http://arxiv.org/abs/2203.00317v2
• Date: Tue, 6 Sep 2022 07:32:49 GMT
• Title: Explainability for identification of vulnerable groups in machine learning models
• Authors: Inga Str\"umke, and Marija Slavkovik
• Abstract summary: Machine learning fairness as a field is focused on the just treatment of individuals and groups under information processing. This raises new challenges on how and when to protect vulnerable individuals and groups under machine learning. Neither existing fairness nor existing explainability methods allow us to ascertain if a prediction model identifies vulnerability.
• Score: 1.7403133838762446
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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