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Found 3 papers, 1 papers with code
Within-group fairness: A guidance for more sound between-group fairness
We introduce a new concept of fairness so-called within-group fairness which requires that AI models should be fair for those in a same sensitive group as well as those in different sensitive groups.
SLIDE: a surrogate fairness constraint to ensure fairness consistency
As they have a vital effect on social decision makings, AI algorithms should be not only accurate and but also fair.
$L_q$ regularization for Fairness AI robust to sampling bias
To ensure fairness on test data, we develop computationally efficient learning algorithms robust to sampling bias.
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