Privacy Constrained Fairness Estimation for Decision Trees. (arXiv:2312.08413v1 [cs.LG])

The protection of sensitive data becomes more vital, as data increases in
value and potency. Furthermore, the pressure increases from regulators and
society on model developers to make their Artificial Intelligence (AI) models
non-discriminatory. To boot, there is a need for interpretable, transparent AI
models for high-stakes tasks. In general, measuring the fairness of any AI
model requires the sensitive attributes of the individuals in the dataset, thus
raising privacy concerns. In this work, the trade-offs between fairness,
privacy and …

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