LUCID: Exposing Algorithmic Bias through Inverse Design. (arXiv:2208.12786v1 [cs.LG])

AI systems can create, propagate, support, and automate bias in
decision-making processes. To mitigate biased decisions, we both need to
understand the origin of the bias and define what it means for an algorithm to
make fair decisions. Most group fairness notions assess a model's equality of
outcome by computing statistical metrics on the outputs. We argue that these
output metrics encounter intrinsic obstacles and present a complementary
approach that aligns with the increasing focus on equality of treatment. By …

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