Abstract
Fairness in Artificial Intelligence is a major requirement for trust in ML-supported decision making. Up to now fairness analysis depends on human interaction – for example the specification of relevant attributes to consider. In this paper we propose a subgroup detection method based on clustering to automate this process. We analyse 10 (sub-)clustering approaches with three fairness metrics on three datasets and identify SLINK as an optimal candidate for subgroup detection.
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Schäfer, J., Wiese, L. (2022). Clustering-Based Subgroup Detection for Automated Fairness Analysis. In: Chiusano, S., et al. New Trends in Database and Information Systems. ADBIS 2022. Communications in Computer and Information Science, vol 1652. Springer, Cham. https://doi.org/10.1007/978-3-031-15743-1_5
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DOI: https://doi.org/10.1007/978-3-031-15743-1_5
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