Scripts for All The Cool Kids, How Do They Fit In

Ekstrand, Michael D.; Tian, Mucun; Azpiazu, Ion Madrazo; Ekstrand, Jennifer D.; Anuyah, Oghenemaro; McNeill, David; Pera, Maria Soledad

doi:10.18122/b2gm6f

Cited by 12 publications

(16 citation statements)

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“…Recommendation algorithms might lead to biased recommendations for any of such stakeholders. For instance, some recent studies 32,33 have explored that the accuracy of recommender systems might vary for specific groups of users. As another example, the authors have acknowledged in Reference 32 that users of different genders are exposed to recommendations with varying accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…For instance, some recent studies 32,33 have explored that the accuracy of recommender systems might vary for specific groups of users. As another example, the authors have acknowledged in Reference 32 that users of different genders are exposed to recommendations with varying accuracy. The reasons for such gender discrimination of the recommenders are also later investigated by considering possible attributes related to the rating profiles of users, such as the degree of anomalous rating behavior 34 .…”

Section: Related Workmentioning

confidence: 99%

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Popularity bias in personality perspective: An analysis of how personality traits expose individuals to the unfair recommendation

Yalçin

Bilge

2023

Concurrency and Computation

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Recommender systems are subject to well-known popularity bias issues, that is, they expose frequently rated items more in recommendation lists than less-rated ones. Such a problem could also have varying effects on users with different gender, age, or rating behavior, which significantly diminishes the users' overall satisfaction with recommendations. In this paper, we approach the problem from the view of user personalities for the first time and discover how users are inclined toward popular items based on their personality traits. More importantly, we analyze the potential unfairness concerns for users with different personalities, which the popularity bias of the recommenders might cause. To this end, we split users into groups of high, moderate, and low clusters in terms of each personality trait in the big-five factor model and investigate how the popularity bias impacts such groups differently by considering several criteria. The experiments conducted with 10 well-known algorithms of different kinds have concluded that less-extroverted people and users avoiding new experiences are exposed to more unfair recommendations regarding popularity, despite being the most significant contributors to the system. However, discrepancies in other qualities of the recommendations for these user characteristics, such as accuracy, diversity, and novelty, vary depending on the utilized algorithm.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Popularity bias in personality perspective: An analysis of how personality traits expose individuals to the unfair recommendation

Yalçin

Bilge

2023

Concurrency and Computation

View full text Add to dashboard Cite

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“…In particular, popularity can be a valuable signal of item quality when exploration costs are in an intermediate regime [9]. Although, ranking by popularity can also result in popularity feedback loops alongside recommendations that serve the majority well while performing poorly for users who are in the minority along some axis [10].…”

Section: Technical Biasesmentioning

confidence: 99%

The AI Learns to Lie to Please You: Preventing Biased Feedback Loops in Machine-Assisted Intelligence Analysis

Stray

2023

Analytics

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Researchers are starting to design AI-powered systems to automatically select and summarize the reports most relevant to each analyst, which raises the issue of bias in the information presented. This article focuses on the selection of relevant reports without an explicit query, a task known as recommendation. Drawing on previous work documenting the existence of human-machine feedback loops in recommender systems, this article reviews potential biases and mitigations in the context of intelligence analysis. Such loops can arise when behavioral “engagement” signals such as clicks or user ratings are used to infer the value of displayed information. Even worse, there can be feedback loops in the collection of intelligence information because users may also be responsible for tasking collection. Avoiding misalignment feedback loops requires an alternate, ongoing, non-engagement signal of information quality. Existing evaluation scales for intelligence product quality and rigor, such as the IC Rating Scale, could provide ground-truth feedback. This sparse data can be used in two ways: for human supervision of average performance and to build models that predict human survey ratings for use at recommendation time. Both techniques are widely used today by social media platforms. Open problems include the design of an ideal human evaluation method, the cost of skilled human labor, and the sparsity of the resulting data.

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“…In particular, data from the online music service Last.fm 4 is a widely used resource for many in the RS community (e.g. Vargas and Castells, 2011;Ribeiro et al, 2015;Kapoor et al, 2015;Ekstrand et al, 2018). Nonetheless, focusing on the MIR literature, we can have a more detailed understanding of how item diversity has been approached in the music domain.…”

Section: Poietic Domain -The Item Sidementioning

confidence: 99%