FALE: Fairness-Aware ALE Plots for Auditing Bias in Subgroups
arxiv(2024)
摘要
Fairness is steadily becoming a crucial requirement of Machine Learning (ML)
systems. A particularly important notion is subgroup fairness, i.e., fairness
in subgroups of individuals that are defined by more than one attributes.
Identifying bias in subgroups can become both computationally challenging, as
well as problematic with respect to comprehensibility and intuitiveness of the
finding to end users. In this work we focus on the latter aspects; we propose
an explainability method tailored to identifying potential bias in subgroups
and visualizing the findings in a user friendly manner to end users. In
particular, we extend the ALE plots explainability method, proposing FALE
(Fairness aware Accumulated Local Effects) plots, a method for measuring the
change in fairness for an affected population corresponding to different values
of a feature (attribute). We envision FALE to function as an efficient, user
friendly, comprehensible and reliable first-stage tool for identifying
subgroups with potential bias issues.
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