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Found 4 papers, 1 papers with code
On the Impact of Output Perturbation on Fairness in Binary Linear Classification
We theoretically study how differential privacy interacts with both individual and group fairness in binary linear classification.
On Fair Selection in the Presence of Implicit and Differential Variance
In the second setting (with known variances), imposing the $\gamma$-rule decreases the utility but we prove a bound on the utility loss due to the fairness mechanism.
On Fair Selection in the Presence of Implicit Variance
We then compare the utility obtained by imposing a fairness mechanism that we term $\gamma$-rule (it includes demographic parity and the four-fifths rule as special cases), to that of a group-oblivious selection algorithm that picks the candidates with the highest estimated quality independently of their group.
The Price of Local Fairness in Multistage Selection
In particular, our experiments show that the price of local fairness is generally smaller when the sensitive attribute is observed at the first stage; but globally fair selections are more locally fair when the sensitive attribute is observed at the second stage---hence in both cases it is often possible to have a selection that has a small price of local fairness and is close to locally fair.
