Facets of Fairness in Search and Recommendation

Verma, Sahil; Gao, Ruoyuan; Shah, Chirag

doi:10.1007/978-3-030-52485-2_1

Cited by 14 publications

(2 citation statements)

References 19 publications

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“…Fairness, in general, is fundamentally a societal construct or a human value, which has been discussed for centuries in many disciplines like philosophy and moral ethics, sociology, law, or economics. Correspondingly, countless definitions of fairness were proposed in different contexts, see for example (Verma and Rubin 2018;Verma et al 2020) for a high-level discussion of the definition of fairness in machine learning and ranking algorithms, or Mulligan et al (2019) for the relationship to social science conception of fairness. As we will see in the remainder of this survey, fairness is a complex concept with multiple perspectives.…”

Section: Notions Of Fairnessmentioning

confidence: 99%

Fairness in recommender systems: research landscape and future directions

Deldjoo

Jannach

Bellogín

et al. 2023

User Model User-Adap Inter

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Recommender systems can strongly influence which information we see online, e.g., on social media, and thus impact our beliefs, decisions, and actions. At the same time, these systems can create substantial business value for different stakeholders. Given the growing potential impact of such AI-based systems on individuals, organizations, and society, questions of fairness have gained increased attention in recent years. However, research on fairness in recommender systems is still a developing area. In this survey, we first review the fundamental concepts and notions of fairness that were put forward in the area in the recent past. Afterward, through a review of more than 160 scholarly publications, we present an overview of how research in this field is currently operationalized, e.g., in terms of general research methodology, fairness measures, and algorithmic approaches. Overall, our analysis of recent works points to certain research gaps. In particular, we find that in many research works in computer science, very abstract problem operationalizations are prevalent and questions of the underlying normative claims and what represents a fair recommendation in the context of a given application are often not discussed in depth. These observations call for more interdisciplinary research to address fairness in recommendation in a more comprehensive and impactful manner.

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Section: Notions Of Fairnessmentioning

confidence: 99%

Fairness in recommender systems: research landscape and future directions

Deldjoo

Jannach

Bellogín

et al. 2023

User Model User-Adap Inter

View full text Add to dashboard Cite

show abstract

“…Fairness, in general, fundamentally is a societal construct or a human value, which has been discussed for centuries in many disciplines like philosophy and moral ethics, sociology, law, or economics. Correspondingly, countless definitions of fairness were proposed in different contexts, see for example Verma et al [16,17] for a high-level discussion of the definition of fairness in machine learning and ranking algorithms, or Mulligan et al [18] for the relationship to social science conception of fairness.…”

Section: Notions Of Fairnessmentioning

confidence: 99%

Fairness in Recommender Systems: Research Landscape and Future Directions

Deldjoo¹,

Jannach²,

Bellogín³

et al. 2022

Preprint

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Recommender systems can strongly influence which information we see online, e.g, on social media, and thus impact our beliefs, decisions, and actions. At the same time, these systems can create substantial business value for different stakeholders. Given the growing potential impact of such AI-based systems on individuals, organizations, and society, questions of fairness have gained increased attention in recent years. However, research on fairness in recommender systems is still a developing area. In this survey, we first review the fundamental concepts and notions of fairness that were put forward in the area in the recent past. Afterwards, we provide a survey of how research in this area is currently operationalized, for example, in terms of the general research methodology, fairness metrics, and algorithmic approaches. Overall, our analysis of recent works points to certain research gaps. In particular, we find that in many research works in computer science very abstract problem operationalizations are prevalent, which circumvent the fundamental and important question of what represents a fair recommendation in the context of a given application.

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