Details
| Original language | English |
|---|---|
| Title of host publication | FAccT '22 |
| Subtitle of host publication | 2022 ACM Conference on Fairness, Accountability, and Transparency |
| Publisher | Association for Computing Machinery (ACM) |
| Pages | 1929-1942 |
| Number of pages | 14 |
| ISBN (electronic) | 9781450393522 |
| Publication status | Published - 20 Jun 2022 |
| Event | 5th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022 - Virtual, Online, Korea, Republic of Duration: 21 Jun 2022 → 24 Jun 2022 |
Publication series
| Name | ACM International Conference Proceeding Series |
|---|
Abstract
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 approaches miss: 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.
Keywords
- Algorithmic Impact Assessment, Exposure, Fairness, Ranking, Recommendation, Strategic Behaviour
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Human-Computer Interaction
- Computer Science(all)
- Computer Vision and Pattern Recognition
- Computer Science(all)
- Computer Networks and Communications
Cite this
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FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency. Association for Computing Machinery (ACM), 2022. p. 1929-1942 (ACM International Conference Proceeding Series).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Fair ranking
T2 - 5th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022
AU - Patro, Gourab K.
AU - Porcaro, Lorenzo
AU - Mitchell, Laura
AU - Zhang, Qiuyue
AU - Zehlike, Meike
AU - Garg, Nikhil
N1 - Funding Information: The authors would like to thank Francesco Fabbri, Jessie Finocchiaro, Faidra Monachou, Ignacio Rios, and Ana-Andreea Stoica for helpful comments. This project has been a part of the MD4SG working group on Bias, Discrimination, and Fairness. This research has received funding under European Research Council (ERC) Marie Sklodowska-Curie grant (agreement no. 860630) for the project No- BIAS, Spanish Ministerio de Ciencia, Innovacion y Universidades (MCIU) and the Agencia Estatal de Investigacion (AEI)-PID2019- 111403GB-I00/AEI/10.13039/501100011033. G. K Patro acknowledges the support by TCS Research fellowship.
PY - 2022/6/20
Y1 - 2022/6/20
N2 - 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 approaches miss: 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.
AB - 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 approaches miss: 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.
KW - Algorithmic Impact Assessment
KW - Exposure
KW - Fairness
KW - Ranking
KW - Recommendation
KW - Strategic Behaviour
UR - http://www.scopus.com/inward/record.url?scp=85133030574&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2201.12662
DO - 10.48550/arXiv.2201.12662
M3 - Conference contribution
AN - SCOPUS:85133030574
T3 - ACM International Conference Proceeding Series
SP - 1929
EP - 1942
BT - FAccT '22
PB - Association for Computing Machinery (ACM)
Y2 - 21 June 2022 through 24 June 2022
ER -