Fairness Vs. Personalization: Towards Equity in Epistemic Utility

• URL: http://arxiv.org/abs/2309.11503v1
• Date: Tue, 5 Sep 2023 18:19:57 GMT
• Title: Fairness Vs. Personalization: Towards Equity in Epistemic Utility
• Authors: Jennifer Chien, David Danks
• Abstract summary: We explicate the inherent tension between personalization and conventional implementations of fairness. We provide a mapping between goals and practical implementations and detail policy recommendations across key stakeholders.
• Score: 2.050345881732981
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The applications of personalized recommender systems are rapidly expanding: encompassing social media, online shopping, search engine results, and more. These systems offer a more efficient way to navigate the vast array of items available. However, alongside this growth, there has been increased recognition of the potential for algorithmic systems to exhibit and perpetuate biases, risking unfairness in personalized domains. In this work, we explicate the inherent tension between personalization and conventional implementations of fairness. As an alternative, we propose equity to achieve fairness in the context of epistemic utility. We provide a mapping between goals and practical implementations and detail policy recommendations across key stakeholders to forge a path towards achieving fairness in personalized systems.

Related papers

• Social Choice for Heterogeneous Fairness in Recommendation [9.753088666705985]
  Algorithmic fairness in recommender systems requires close attention to the needs of a diverse set of stakeholders. Previous work has often been limited by fixed, single-objective definitions of fairness. Our work approaches recommendation fairness from the standpoint of computational social choice.
  arXiv  Detail & Related papers  (2024-10-06T17:01:18Z)
• Bypassing the Popularity Bias: Repurposing Models for Better Long-Tail Recommendation [0.0]
  We aim to achieve a more equitable distribution of exposure among publishers on an online content platform. We propose a novel approach of repurposing existing components of an industrial recommender system to deliver valuable exposure to underrepresented publishers.
  arXiv  Detail & Related papers  (2024-09-17T15:40:55Z)
• Improving Point-based Crowd Counting and Localization Based on Auxiliary Point Guidance [59.71186244597394]
  We introduce an effective approach to stabilize the proposal-target matching in point-based methods. We propose Auxiliary Point Guidance (APG) to provide clear and effective guidance for proposal selection and optimization. We also develop Implicit Feature Interpolation (IFI) to enable adaptive feature extraction in diverse crowd scenarios.
  arXiv  Detail & Related papers  (2024-05-17T07:23:27Z)
• A Survey on Fairness-aware Recommender Systems [59.23208133653637]
  We present concepts of fairness in different recommendation scenarios, comprehensively categorize current advances, and introduce typical methods to promote fairness in different stages of recommender systems. Next, we delve into the significant influence that fairness-aware recommender systems exert on real-world industrial applications.
  arXiv  Detail & Related papers  (2023-06-01T07:08:22Z)
• Fairness in Matching under Uncertainty [78.39459690570531]
  algorithmic two-sided marketplaces have drawn attention to the issue of fairness in such settings. We axiomatize a notion of individual fairness in the two-sided marketplace setting which respects the uncertainty in the merits. We design a linear programming framework to find fair utility-maximizing distributions over allocations.
  arXiv  Detail & Related papers  (2023-02-08T00:30:32Z)
• Joint Multisided Exposure Fairness for Recommendation [76.75990595228666]
  This paper formalizes a family of exposure fairness metrics that model the problem jointly from the perspective of both the consumers and producers. Specifically, we consider group attributes for both types of stakeholders to identify and mitigate fairness concerns that go beyond individual users and items towards more systemic biases in recommendation.
  arXiv  Detail & Related papers  (2022-04-29T19:13:23Z)
• Fairness, Welfare, and Equity in Personalized Pricing [88.9134799076718]
  We study the interplay of fairness, welfare, and equity considerations in personalized pricing based on customer features. We show the potential benefits of personalized pricing in two settings: pricing subsidies for an elective vaccine, and the effects of personalized interest rates on downstream outcomes in microcredit.
  arXiv  Detail & Related papers  (2020-12-21T01:01:56Z)
• HyperFair: A Soft Approach to Integrating Fairness Criteria [17.770533330914102]
  We introduce HyperFair, a framework for enforcing soft fairness constraints in a hybrid recommender system. We propose two ways to employ the methods we introduce: first as an extension of a probabilistic soft logic recommender system template. We empirically validate our approach by implementing multiple HyperFair hybrid recommenders and compare them to a state-of-the-art fair recommender.
  arXiv  Detail & Related papers  (2020-09-05T05:00:06Z)
• PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest [54.56236567783225]
  PinnerSage is an end-to-end recommender system that represents each user via multi-modal embeddings. We conduct several offline and online A/B experiments to show that our method significantly outperforms single embedding methods.
  arXiv  Detail & Related papers  (2020-07-07T17:13:20Z)
• Achieving Fairness via Post-Processing in Web-Scale Recommender Systems [6.5191290612443105]
  We extend the definitions of fairness to recommender systems, namely equality of opportunity and equalized odds. We propose scalable methods for achieving equality of opportunity and equalized odds in rankings in the presence of position bias.
  arXiv  Detail & Related papers  (2020-06-19T20:12:13Z)
• Opportunistic Multi-aspect Fairness through Personalized Re-ranking [5.8562079474220665]
  We present a re-ranking approach to fairness-aware recommendation that learns individual preferences across multiple fairness dimensions. We show that our opportunistic and metric-agnostic approach achieves a better trade-off between accuracy and fairness than prior re-ranking approaches.
  arXiv  Detail & Related papers  (2020-05-21T04:25:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.