Pairwise Fairness for Ranking and Regression

12 Jun 2019  ·  Harikrishna Narasimhan, Andrew Cotter, Maya Gupta, Serena Wang ·

We present pairwise fairness metrics for ranking models and regression models that form analogues of statistical fairness notions such as equal opportunity, equal accuracy, and statistical parity. Our pairwise formulation supports both discrete protected groups, and continuous protected attributes. We show that the resulting training problems can be efficiently and effectively solved using existing constrained optimization and robust optimization techniques developed for fair classification. Experiments illustrate the broad applicability and trade-offs of these methods.

PDF Abstract


Introduced in the Paper:

Business Matching

Used in the Paper:

W3C Experts Wiki Talk Page Comments

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.


No methods listed for this paper. Add relevant methods here