A Multi-Group Approach To Algorithmic Fairness

Guy Rothblum (Weizmann Institute of Science)
simons.berkeley.edu/talks/mul...
Algorithmic Aspects of Causal Inference
As algorithms increasingly inform and influence decisions made about individuals, it becomes increasingly important to address concerns that these algorithms might be discriminatory. We develop and study multi-group fairness, a new approach to algorithmic fairness that aims to provide fairness guarantees for every subpopulation in a rich class of overlapping subgroups. We focus on guarantees that are aligned with obtaining predictions that are accurate w.r.t. the training data, such as subgroup calibration or subgroup loss-minimization. We present new algorithms for learning multi-group fair predictors, study the computational complexity of this task, and draw connections to the theory of agnostic learning.