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Who Pays? Personalization, Bossiness and the Cost of Fairness

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arxiv 2209.04043 v1 pith:AFWOFOOD submitted 2022-09-08 cs.IR cs.AIcs.GT

classification cs.IRcs.AIcs.GT
keywords fairnesscostbossinessconcernfairness-awareincentiveotherspersonalization
verification ladder T0 review T1 audit T2 compute T3 formal

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Fairness-aware recommender systems that have a provider-side fairness concern seek to ensure that protected group(s) of providers have a fair opportunity to promote their items or products. There is a ``cost of fairness'' borne by the consumer side of the interaction when such a solution is implemented. This consumer-side cost raises its own questions of fairness, particularly when personalization is used to control the impact of the fairness constraint. In adopting a personalized approach to the fairness objective, researchers may be opening their systems up to strategic behavior on the part of users. This type of incentive has been studied in the computational social choice literature under the terminology of ``bossiness''. The concern is that a bossy user may be able to shift the cost of fairness to others, improving their own outcomes and worsening those for others. This position paper introduces the concept of bossiness, shows its application in fairness-aware recommendation and discusses strategies for reducing this strategic incentive.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. User-item fairness tradeoffs in recommendations

    cs.IR 2024-12 conditional novelty 6.0 of 10

    The price of item fairness in recommendations falls as user preferences become more diverse, but rises sharply for users whose preferences are misestimated.

  2. Envy-Free but Still Unfair: Envy-Freeness Up To One Item (EF-1) in Personalized Recommendation

    cs.IR 2025-09 conditional novelty 4.0 of 10

    Envy-freeness (EF1) can hold even when personalized recommendations are highly unfair to a minority group, so it is an insufficient fairness metric.

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