Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | FAccT '22 |
| Untertitel | 2022 ACM Conference on Fairness, Accountability, and Transparency |
| Herausgeber (Verlag) | Association for Computing Machinery (ACM) |
| Seiten | 1929-1942 |
| Seitenumfang | 14 |
| ISBN (elektronisch) | 9781450393522 |
| Publikationsstatus | Veröffentlicht - 20 Juni 2022 |
| Veranstaltung | 5th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022 - Virtual, Online, Südkorea Dauer: 21 Juni 2022 → 24 Juni 2022 |
Publikationsreihe
| 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.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Software
- Informatik (insg.)
- Mensch-Maschine-Interaktion
- Informatik (insg.)
- Maschinelles Sehen und Mustererkennung
- Informatik (insg.)
- Computernetzwerke und -kommunikation
Zitieren
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- BibTex
- RIS
FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency. Association for Computing Machinery (ACM), 2022. S. 1929-1942 (ACM International Conference Proceeding Series).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › 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 -