A new research paper titled "Scoring Is Not Enough: Addressing Gaps in Utility-fairness Trade-offs for Ranking" argues that current scoring functions in information retrieval and recommendation systems are insufficient for balancing utility and fairness. The paper demonstrates through counter-examples that scoring alone is sub-optimal for achieving desired utility-fairness trade-offs, regardless of whether the scoring is deterministic or randomized, or measured at a single or multiple query scope. The research suggests that semi-greedy post-processing methods show promise in achieving better trade-offs, approaching the ideal of exhaustive post-processing in a practical manner. AI
IMPACT Highlights limitations in current AI ranking algorithms, suggesting new approaches for fairer and more useful outcomes.
RANK_REASON The cluster contains a research paper published on arXiv.
Read on arXiv cs.IR (Information Retrieval) →
- arXiv
- Hugging Face
- information retrieval
- Scoring Is Not Enough: Addressing Gaps in Utility-fairness Trade-offs for Ranking
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