Operationalizing Individual Fairness via Gradient Descent and Bradley-Terry Models
Individual fairness, the notion that "similar individuals should be treated similarly," provides a strong and flexible fairness guarantee for algorithmic decision makers. However, a barrier to implementing individual fairness in practice is the difficulty of learning the similarity metric over individuals. In this work, we present an algorithm for learning a Mahalanobis similarity metric from triplet queries of the form "is individual $i$ more similar to individual $j$ or $k$?" We work in the standard Bradley-Terry model for pairwise comparisons. Our algorithm consists of a spectral initialization step followed by gradient descent. We provide extensive theoretical guarantees on our algori...
| Authors | Conlan Olson, Linjun Zhang, Zhun Deng et al. |
|---|---|
| Primary category | stat.ML |
| Published | 22 May 2026 |
| Last updated at source | 7 July 2026 |
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