Incentives for Item Duplication Under Fair Ranking Policies

Nunzio, Giorgio Maria Di; Fabris, Alessandro; Silvello, Gianmaria; Susto, Gian Antonio

doi:10.1007/978-3-030-78818-6_7

Cited by 2 publications

(3 citation statements)

References 16 publications

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“…Strategic providers can create duplicates or near-duplicates-possibly hard to automatically identifyof their existing offerings in a ranking system. Since certain fair ranking mechanisms may try to ensure benefits for all listed items, providers with more copies of same items stand to gain more benefits [57,43]. We give such an example in Appendix Table 4.…”

Section: Strategic Behaviormentioning

confidence: 99%

“…on the RS to improve their ranking [175], influencers on social network sites could try to hijack popular trends [65,32]. Providers can even strategically exploit the deployed fair ranking mechanisms to extract more benefits [57,43]. Not factoring in such strategic behavior could impact ranking and recommendation systems, and especially the performance of fair ranking mechanisms.…”

Section: Strategic Behaviormentioning

confidence: 99%

“…A way to avoid such outcomes, would be to design and use suitable context-specific weighting mechanisms for rankings which can anticipate and account for such temporal variations. [43] and the Sybil attacks in networks [65]): Here, the table (a) has a list of relevant items for certain information need. The list contains two items each from providers A and B.…”

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confidence: 99%

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Fair ranking: a critical review, challenges, and future directions

Patro¹,

Porcaro²,

Mitchell³

et al. 2022

Preprint

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Ranking, recommendation, and retrieval systems are widely used in online platforms and other societal systems, including e-commerce, media-streaming, admissions, gig platforms, and hiring. In the recent past, a large "fair ranking" research literature has been developed around making these systems fair to the individuals, providers, or content that are being ranked. Most of this literature defines fairness for a single instance of retrieval, or as a simple additive notion for multiple instances of retrievals over time. This work provides a critical overview of this literature, detailing the often context-specific concerns that such an approach misses: the gap between high ranking placements and true provider utility, spillovers and compounding effects over time, induced strategic incentives, and the effect of statistical uncertainty. We then provide a path forward for a more holistic and impact-oriented fair ranking research agenda, including methodological lessons from other fields and the role of the broader stakeholder community in overcoming data bottlenecks and designing effective regulatory environments.

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Section: Strategic Behaviormentioning

confidence: 99%

Section: Strategic Behaviormentioning

confidence: 99%

See 1 more Smart Citation

Fair ranking: a critical review, challenges, and future directions

Patro¹,

Porcaro²,

Mitchell³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

A Systematic Review of Fairness, Accountability, Transparency and Ethics in Information Retrieval

Bernard,

Balog

2023

ACM Comput. Surv.

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We live in an information society that strongly relies on information retrieval systems, such as search engines and conversational assistants. Consequently, the trustworthiness of these systems is of critical importance, and has attracted a significant research attention in recent years. In this work, we perform a systematic literature review of the field of fairness, accountability, transparency, and ethics in information retrieval. In particular, we investigate the definitions, approaches, and evaluation methodologies proposed to build trustworthy information retrieval systems. This review reveals the lack of standard definitions, arguably due to the multi-dimensional nature of the different notions. In terms of approaches, most of the work focuses on building either a fair or a transparent information retrieval system. As for evaluation, fairness is often assessed by means of automatic evaluation, while accountability and transparency are most commonly evaluated using audits and user studies. Based on the surveyed literature, we develop taxonomies of requirements for the different notions, and further use these taxonomies to propose practical definitions to quantify the degree to which an information retrieval system satisfies a given notion. Finally, we discuss challenges that have yet to be solved for information retrieval systems to be trustworthy.

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Incentives for Item Duplication Under Fair Ranking Policies

Cited by 2 publications

References 16 publications

Fair ranking: a critical review, challenges, and future directions

Fair ranking: a critical review, challenges, and future directions

A Systematic Review of Fairness, Accountability, Transparency and Ethics in Information Retrieval

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