The multisided complexity of fairness in recommender systems

@article{Sonboli2022TheMC,
  title={The multisided complexity of fairness in recommender systems},
  author={Nasim Sonboli and Robin D. Burke and Michael D. Ekstrand and Rishabh Mehrotra},
  journal={AI Magazine},
  year={2022}
}
Recommender systems are poised at the interface between stakeholders: for example, job applicants and employers in the case of recommendations of employment listings, or artists and listeners in the case of music recommendation. In such multisided platforms, recommender systems play a key role in enabling discovery of products and information at large scales. However, as they have become more and more pervasive in society, the equitable distribution of their benefits and harms have been… 

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References

SHOWING 1-10 OF 85 REFERENCES

Opportunistic Multi-aspect Fairness through Personalized Re-ranking

TLDR
It is shown that the opportunistic and metric-agnostic approach achieves a better trade-off between accuracy and fairness than prior re-ranking approaches and does so across multiple fairness dimensions.

Fairness and Transparency in Recommendation: The Users’ Perspective

TLDR
An exploratory interview study that investigates user perceptions of fairness, recommender systems, and fairness-aware objectives is described and three features are proposed – informed by the needs of the authors' participants – that could improve user understanding of and trust in Fairness-aware recommender Systems.

Fairness Aware Recommendations on Behance

TLDR
This work proposes a re-ranking strategy that can be applied to the scored recommendation lists to improve exposure distribution across the creators (thereby improving the fairness), without unduly affecting the relevance of recommendations provided to the consumers.

Personalized fairness-aware re-ranking for microlending

TLDR
A Fairness-Aware Re-ranking (FAR) algorithm to balance ranking quality and borrower-side fairness, which can significantly promote fairness with little sacrifice in accuracy, and be attentive to individual lender preference on loan diversity.

Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems

TLDR
This paper provides theory for a set of conditions under which fairness of individual models does compose, and presents an analytical framework for both understanding whether a real system's signals can achieve compositional fairness, and improving which component would have the greatest impact on the fairness of the overall system.

Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search

TLDR
This work presents a framework for quantifying and mitigating algorithmic bias in mechanisms designed for ranking individuals, typically used as part of web-scale search and recommendation systems, and is the first large-scale deployed framework for ensuring fairness in the hiring domain.

Equity of Attention: Amortizing Individual Fairness in Rankings

TLDR
The challenge of achieving amortized individual fairness subject to constraints on ranking quality as an online optimization problem is formulated and solved as an integer linear program and it is demonstrated that the method can improve individual fairness while retaining high ranking quality.

Break the Loop: Gender Imbalance in Music Recommenders

TLDR
This work analyzes a widely-used collaborative filtering approach with two public datasets-enriched with gender information-to understand how this approach performs with respect to the artists' gender and proposes a progressive re-ranking method based on the insights from the interviews.

Multistakeholder recommendation: Survey and research directions

TLDR
The multistakeholder perspective on recommendation is outlined, highlighting example research areas and discussing important issues, open questions, and prospective research directions.

Popularity Bias in Recommendation: A Multi-stakeholder Perspective

TLDR
This dissertation, which studies the impact of popularity bias in recommender systems from a multi-stakeholder perspective, proposes several algorithms each approaching the popularity bias mitigation from a different angle and compares their performances using several metrics with some other state-of-the-art approaches in the literature.
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