Fairness in Information Access Systems

Ekstrand, Michael D.; Das, Anubrata; Burke, Robin; Diaz, Fernando

doi:10.48550/arxiv.2105.05779

Cited by 7 publications

(14 citation statements)

References 105 publications

(187 reference statements)

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“…This, in turn, then may lead to biased recommendations when machine learning models reflect or reinforce the bias, as mentioned above. In works that address this problem, the term bias is often used in a more statistical sense as done in [20]. However, the use of the term is not consistent in the literature, as observed also in [21] and in our work.…”

Section: Related Concepts: Responsible Recommendation and Biasesmentioning

confidence: 64%

“…Fairness is therefore seen as a particular aspect of responsible recommendation in [19]. A similar view is taken in [20], where the authors review a number of related concerns of responsibility: accountability, transparency, safety, privacy, and ethics. In the context of our present work, most of these concepts are however only of secondary interest.…”

Section: Related Concepts: Responsible Recommendation and Biasesmentioning

confidence: 96%

“…Issues of fairness are often discussed within the broader area of responsible recommendation [19,20]. In [19], the authors in particular discuss potential negative effects of recommendations and their underlying reasons with a focus on the media domain.…”

Section: Related Concepts: Responsible Recommendation and Biasesmentioning

confidence: 99%

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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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Section: Related Concepts: Responsible Recommendation and Biasesmentioning

confidence: 64%

Section: Related Concepts: Responsible Recommendation and Biasesmentioning

confidence: 96%

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Fairness in Recommender Systems: Research Landscape and Future Directions

Deldjoo¹,

Jannach²,

Bellogín³

et al. 2022

Preprint

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“…The cost to publishers due to under-exposure of their content can be further aggravated by superstar economics, common in music and other recommendation scenarios [7,16,33,37]. For an overview of fairness and bias in recommender systems, we point the reader to a recent survey by Chen et al [9], Ekstrand et al [13].…”

Section: Related Workmentioning

confidence: 99%

Revisiting Popularity and Demographic Biases in Recommender Evaluation and Effectiveness

Neophytou¹,

Mitra²,

Stinson³

2021

Preprint

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Recommendation algorithms are susceptible to popularity bias: a tendency to recommend popular items even when they fail to meet user needs. A related issue is that the recommendation quality can vary by demographic groups. Marginalized groups or groups that are under-represented in the training data may receive less relevant recommendations from these algorithms compared to others. In a recent study, Ekstrand et al. [15] investigate how recommender performance varies according to popularity and demographics, and find statistically significant differences in recommendation utility between binary genders on two datasets, and significant effects based on age on one dataset. Here we reproduce those results and extend them with additional analyses. We find statistically significant differences in recommender performance by both age and gender. We observe that recommendation utility steadily degrades for older users, and is lower for women than men. We also find that the utility is higher for users from countries with more representation in the dataset. In addition, we find that total usage and the popularity of consumed content are strong predictors of recommender performance and also vary significantly across demographic groups.

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“…There has been increased interest in fair ranking systems, as witnessed by the number of publications [14,45], the topic's attention during keynotes leading conferences [7,19], and challenges such as the TREC Fair Ranking track [15]. Several particularities about rankings make this task especially challenging.…”

Section: Introductionmentioning

confidence: 99%

Fairness of Exposure in Light of Incomplete Exposure Estimation

Heuss,

Sarvi,

de Rijke

2022

Preprint

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Fairness of exposure is a commonly used notion of fairness for ranking systems. It is based on the idea that all items or item groups should get exposure proportional to the merit of the item or the collective merit of the items in the group. Often, stochastic ranking policies are used to ensure fairness of exposure. Previous work unrealistically assumes that we can reliably estimate the expected exposure for all items in each ranking produced by the stochastic policy. In this work, we discuss how to approach fairness of exposure in cases where the policy contains rankings of which, due to inter-item dependencies, we cannot reliably estimate the exposure distribution. In such cases, we cannot determine whether the policy can be considered fair. Our contributions in this paper are twofold. First, we define a method called FELIX for finding stochastic policies that avoid showing rankings with unknown exposure distribution to the user without having to compromise user utility or item fairness. Second, we extend the study of fairness of exposure to the top-𝑘 setting and also assess FELIX in this setting. We find that FELIX can significantly reduce the number of rankings with unknown exposure distribution without a drop in user utility or fairness compared to existing fair ranking methods, both for full-length and top-𝑘 rankings. This is an important first step in developing fair ranking methods for cases where we have incomplete knowledge about the user's behaviour. CCS CONCEPTS• Information systems → Evaluation of retrieval results; Retrieval models and ranking.

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Fairness in Information Access Systems

Cited by 7 publications

References 105 publications

Fairness in Recommender Systems: Research Landscape and Future Directions

Fairness in Recommender Systems: Research Landscape and Future Directions

Revisiting Popularity and Demographic Biases in Recommender Evaluation and Effectiveness

Fairness of Exposure in Light of Incomplete Exposure Estimation

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