Seminari CS: Randomization for individual fairness (David Garcia Soriano, UPC)

Randomization for individual fairness
David García Soriano (UPC)

Lloc: Omega S215 Desembre 10, 13:00-14:00

As algorithms are increasingly used to make decisions affecting individuals
(from matching donors to recipients, to ranking candidates, to recommending
routes...), ensuring these decisions are fair has become a critical concern.
Traditional optimization often yields many equally optimal solutions, but
selecting among them fairly is paramount when the outcomes impact people's
lives. This talk explores a unified, principled approach to algorithmic
fairness centered on the individual, using the power of randomization to
provide the strongest possible guarantees.

To this end, we introduce the *distributional maxmin fairness* framework,
grounded in a Rawlsian principle of justice. It ensures a fair selection by
randomizing over valid solutions so as to maximize the minimum probability
that any individual gets a desirable outcome.  Equivalently, a probability
distribution over feasible solutions is maxmin-fair if it is not possible to
improve the satisfaction probability of any individual without decreasing it
for some other individual which is no better off.  We present efficient
algorithms for three core problems: fair matching, fair ranking under group
constraints, and fair route recommendation, showing that our maxmin-fair-by-
design methodology provides a rigorous and practical foundation for building
equitable algorithms.