Scoring Is Not Enough: Addressing Gaps in Utility-fairness Trade-offs for Ranking
Scoring functions are used to represent the relevance of individual documents. In modern information retrieval or recommendation systems, they are often learned from data and play a pivotal role in ranking sets of documents or items in a way that maximizes utility to a query or user. With the recent interest in algorithmic fairness, the success of scoring has naturally led to methods that learn scores that simultaneously trade off fairness and utility. In this work, we show that in stark contrast with utility-centric objectives, scoring is sub-optimal in achieving all utility-fairness trade-offs. We establish this with a series of counter-examples with a generic fairness formulation. We s...
| Authors | Shubham Singh, Ian A. Kash, Mesrob I. Ohannessian |
|---|---|
| Primary category | cs.IR |
| Published | 24 June 2026 |
| Last updated at source | 24 June 2026 |
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