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Found 2 papers, 2 papers with code
LUCID-GAN: Conditional Generative Models to Locate Unfairness
Most group fairness notions detect unethical biases by computing statistical parity metrics on a model's output.
LUCID: Exposing Algorithmic Bias through Inverse Design
The results show that by shifting the focus towards equality of treatment and looking into the algorithm's internal workings, the canonical sets are a valuable addition to the toolbox of algorithmic fairness evaluation.
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