Fair ranking: a critical review, challenges, and future directions
- URL: http://arxiv.org/abs/2201.12662v1
- Date: Sat, 29 Jan 2022 21:26:04 GMT
- Title: Fair ranking: a critical review, challenges, and future directions
- Authors: Gourab K Patro, Lorenzo Porcaro, Laura Mitchell, Qiuyue Zhang, Meike
Zehlike, and Nikhil Garg
- Abstract summary: Large "fair ranking" research literature has been developed around making these systems fair to the individuals, providers, or content that are being ranked.
This work provides a critical overview of this literature, detailing the often context-specific concerns that such an approach misses.
We then provide a path forward for a more holistic and impact-oriented fair ranking research agenda.
- Score: 4.126546022406797
- License: http://creativecommons.org/licenses/by/4.0/
- 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 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.
Related papers
- Online Model-based Anomaly Detection in Multivariate Time Series: Taxonomy, Survey, Research Challenges and Future Directions [0.017476232824732776]
Time-series anomaly detection plays an important role in engineering processes.
This survey introduces a novel taxonomy where a distinction between online and offline, and training and inference is made.
It presents the most popular data sets and evaluation metrics used in the literature, as well as a detailed analysis.
arXiv Detail & Related papers (2024-08-07T13:01:10Z) - Fairness and Bias Mitigation in Computer Vision: A Survey [61.01658257223365]
Computer vision systems are increasingly being deployed in high-stakes real-world applications.
There is a dire need to ensure that they do not propagate or amplify any discriminatory tendencies in historical or human-curated data.
This paper presents a comprehensive survey on fairness that summarizes and sheds light on ongoing trends and successes in the context of computer vision.
arXiv Detail & Related papers (2024-08-05T13:44:22Z) - Bias and Fairness in Large Language Models: A Survey [73.87651986156006]
We present a comprehensive survey of bias evaluation and mitigation techniques for large language models (LLMs)
We first consolidate, formalize, and expand notions of social bias and fairness in natural language processing.
We then unify the literature by proposing three intuitive, two for bias evaluation, and one for mitigation.
arXiv Detail & Related papers (2023-09-02T00:32:55Z) - Consumer-side Fairness in Recommender Systems: A Systematic Survey of
Methods and Evaluation [1.4123323039043334]
Growing awareness of discrimination in machine learning methods motivated both academia and industry to research how fairness can be ensured in recommender systems.
For recommender systems, such issues are well exemplified by occupation recommendation, where biases in historical data may lead to recommender systems relating one gender to lower wages or to the propagation of stereotypes.
This survey serves as a systematic overview and discussion of the current research on consumer-side fairness in recommender systems.
arXiv Detail & Related papers (2023-05-16T10:07:41Z) - The Role of Relevance in Fair Ranking [1.5469452301122177]
We argue that relevance scores should satisfy a set of desired criteria in order to guide fairness interventions.
We then empirically show that not all of these criteria are met in a case study of relevance inferred from biased user click data.
Our analyses and results surface the pressing need for new approaches to relevance collection and generation.
arXiv Detail & Related papers (2023-05-09T16:58:23Z) - Fairness meets Cross-Domain Learning: a new perspective on Models and
Metrics [80.07271410743806]
We study the relationship between cross-domain learning (CD) and model fairness.
We introduce a benchmark on face and medical images spanning several demographic groups as well as classification and localization tasks.
Our study covers 14 CD approaches alongside three state-of-the-art fairness algorithms and shows how the former can outperform the latter.
arXiv Detail & Related papers (2023-03-25T09:34:05Z) - Fairness in Recommender Systems: Research Landscape and Future
Directions [119.67643184567623]
We review the concepts and notions of fairness that were put forward in the area in the recent past.
We present an overview of how research in this field is currently operationalized.
Overall, our analysis of recent works points to certain research gaps.
arXiv Detail & Related papers (2022-05-23T08:34:25Z) - Through the Data Management Lens: Experimental Analysis and Evaluation
of Fair Classification [75.49600684537117]
Data management research is showing an increasing presence and interest in topics related to data and algorithmic fairness.
We contribute a broad analysis of 13 fair classification approaches and additional variants, over their correctness, fairness, efficiency, scalability, and stability.
Our analysis highlights novel insights on the impact of different metrics and high-level approach characteristics on different aspects of performance.
arXiv Detail & Related papers (2021-01-18T22:55:40Z) - Overview of the TREC 2019 Fair Ranking Track [65.15263872493799]
The goal of the TREC Fair Ranking track was to develop a benchmark for evaluating retrieval systems in terms of fairness to different content providers.
This paper presents an overview of the track, including the task definition, descriptions of the data and the annotation process.
arXiv Detail & Related papers (2020-03-25T21:34:58Z)
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.