Fairness and Transparency in Recommendation: The Users’ Perspective

Sonboli, Nasim; Smith, Jessie J.; Berenfus, Florencia Cabral; Burke, Robin; Fiesler, Casey

doi:10.1145/3450613.3456835

Cited by 44 publications

(22 citation statements)

References 42 publications

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“…On the other hand, notable examples of field experiment are provided in [46], where a gender-representative re-ranker is deployed for a randomly chosen 50% of the recruiters on the LinkedIn Recruiter platform (A/B testing), and in [110]. We only found one paper that relied on interviews as a qualitative research method [30]. Also, only very few papers used more than one experiment type, e.g., [123] were both a user study and an offline experiment were conducted.…”

Section: Methodsmentioning

confidence: 99%

“…Figure 2 shows how many papers in our survey were considered as technical and conceptual papers. Non-technical papers cover a wide range of contributions, such as guidelines for designers to avoid compounding previous injustices [29], exploratory studies that investigate user perceptions of fairness [30], or discussions about how difficult it is to audit these types of systems [31]. We observe that today's research on fairness on recommender systems is dominated by technical papers.…”

Section: Types Of Contributionsmentioning

confidence: 99%

See 1 more Smart Citation

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: Methodsmentioning

confidence: 99%

Section: Types Of Contributionsmentioning

confidence: 99%

Fairness in Recommender Systems: Research Landscape and Future Directions

Deldjoo¹,

Jannach²,

Bellogín³

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…Transparency aims to evaluate "whether the explanations can reveal the internal working principles of the recommender models [Tai, 2021;Sonboli, 2021;Li, 2021b]?" It encourages the algorithms to produce explanations which can open the "black box" of the recommender models.…”

Section: Transparencymentioning

confidence: 99%

“…[Hada, 2021;Guesmi, 2021;Gao et al, 2019;Chen et al, 2013] Users Transparency Whether the explanations can reveal the internal working principles of the recommender models? [Chen, 2021a;Sonboli, 2021;Li, 2021c;Li, 2020c;Fu, 2020] Model designers…”

Section: Effectivenessmentioning

confidence: 99%

Measuring "Why" in Recommender Systems: a Comprehensive Survey on the Evaluation of Explainable Recommendation

Chen¹,

Zhang²,

Wen³

2022

Preprint

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Explainable recommendation has shown its great advantages for improving recommendation persuasiveness, user satisfaction, system transparency and among others. A fundamental problem of explainable recommendation is how to evaluate the explanations. In the past few years, various evaluation strategies have been proposed. However, they are scattered in different papers, and there lacks a systematic and detailed comparison between them. To bridge this gap, in this paper, we comprehensively review the previous work, and provide different taxonomies according to the evaluation perspectives and evaluation methods. Beyond summarization, we also analyze the (dis)advantages of existing evaluation methods, and provide a series of guidelines on how to select them. The contents of this survey are concluded from more than 100 papers from top-tier conferences like IJCAI, AAAI, TheWebConf, Recsys, UMAP and IUI, and the complete comparisons are presented at https://shimo.im/sheets/VKrpYTcwVH6KXgdy/MO DOC/. With this survey, we finally aim to provide a clear and comprehensive review on the evaluation of explainable recommendation.

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“…In general, the problem of fairness has received increased attention in recent years in the recommender systems research community. While no consistent definition of fairness is yet established and the perception of fairness can vary across consumers [63], fairness is often considered as the absence of any bias, prejudice, favoritism, mistreatment toward individuals, group, classes, or social categories based on their inherent or acquired characteristics [10]. Often, fairness and unfairness are also related to the problem of (digital) discrimination [25,28], which is often characterized as an unfair or unequal treatment of individuals, groups, classes or social categories according to certain characteristics.…”

Section: User Modeling Personalization and Engagementmentioning

confidence: 99%

Responsible media technology and AI: challenges and research directions

et al. 2021

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The last two decades have witnessed major disruptions to the traditional media industry as a result of technological breakthroughs. New opportunities and challenges continue to arise, most recently as a result of the rapid advance and adoption of artificial intelligence technologies. On the one hand, the broad adoption of these technologies may introduce new opportunities for diversifying media offerings, fighting disinformation, and advancing data-driven journalism. On the other hand, techniques such as algorithmic content selection and user personalization can introduce risks and societal threats. The challenge of balancing these opportunities and benefits against their potential for negative impacts underscores the need for more research in responsible media technology. In this paper, we first describe the major challenges—both for societies and the media industry—that come with modern media technology. We then outline various places in the media production and dissemination chain, where research gaps exist, where better technical approaches are needed, and where technology must be designed in a way that can effectively support responsible editorial processes and principles. We argue that a comprehensive approach to research in responsible media technology, leveraging an interdisciplinary approach and a close cooperation between the media industry and academic institutions, is urgently needed.

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Fairness and Transparency in Recommendation: The Users’ Perspective

Cited by 44 publications

References 42 publications

Fairness in Recommender Systems: Research Landscape and Future Directions

Fairness in Recommender Systems: Research Landscape and Future Directions

Measuring "Why" in Recommender Systems: a Comprehensive Survey on the Evaluation of Explainable Recommendation

Responsible media technology and AI: challenges and research directions

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