#16: Fairness in Recommender Systems with Michael D. Ekstrand
In episode 16 of Recsperts, we hear from Michael D. Ekstrand, Associate Professor at Boise State University, about fairness in recommender systems. We discuss why fairness matters and provide an overview of the multidimensional fairness-aware RecSys landscape. Furthermore, we talk about tradeoffs, methods and receive practical advice on how to get started with tackling unfairness.
In our discussion, Michael outlines the difference and similarity between fairness and bias. We discuss several stages at which biases can enter the system as well as how bias can indeed support mitigating unfairness. We also cover the perspectives of different stakeholders with respect to fairness. We also learn that measuring fairness depends on the specific fairness concern one is interested in and that solving fairness universally is highly unlikely.
Towards the end of the episode, we take a look at further challenges as well as how and where the upcoming RecSys 2023 provides a forum for those interested in fairness-aware recommender systems.
Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.
Links from the Episode:
In our discussion, Michael outlines the difference and similarity between fairness and bias. We discuss several stages at which biases can enter the system as well as how bias can indeed support mitigating unfairness. We also cover the perspectives of different stakeholders with respect to fairness. We also learn that measuring fairness depends on the specific fairness concern one is interested in and that solving fairness universally is highly unlikely.
Towards the end of the episode, we take a look at further challenges as well as how and where the upcoming RecSys 2023 provides a forum for those interested in fairness-aware recommender systems.
Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.
- (00:00) - Episode Overview
- (02:57) - Introduction Michael Ekstrand
- (17:08) - Motivation for Fairness-Aware Recommender Systems
- (25:45) - Overview and Definition of Fairness in RecSys
- (46:51) - Distributional and Representational Harm
- (53:59) - Relationship between Fairness and Bias
- (01:04:43) - Tradeoffs
- (01:13:36) - Methods and Metrics for Fairness
- (01:28:06) - Practical Advice for Tackling Unfairness
- (01:32:24) - Further Challenges
- (01:35:24) - RecSys 2023
- (01:38:29) - Closing Remarks
Links from the Episode:
- Michael Ekstrand on LinkedIn
- Michael Ekstrand on Mastodon
- Michael's Website
- GroupLens Lab at University of Minnesota
- People and Information Research Team (PIReT)
- 6th FAccTRec Workshop: Responsible Recommendation
- NORMalize: The First Workshop on Normative Design and Evaluation of Recommender Systems
- ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT)
- Coursera: Recommender Systems Specialization
- LensKit: Python Tools for Recommender Systems
- Chris Anderson - The Long Tail: Why the Future of Business Is Selling Less of More
- Fairness in Recommender Systems (in Recommender Systems Handbook)
- Ekstrand et al. (2022): Fairness in Information Access Systems
- Keynote at EvalRS (CIKM 2022): Do You Want To Hunt A Kraken? Mapping and Expanding Recommendation Fairness
- Friedler et al. (2021): The (Im)possibility of Fairness: Different Value Systems Require Different Mechanisms For Fair Decision Making
- Safiya Umoja Noble (2018): Algorithms of Oppression: How Search Engines Reinforce Racism
Papers:
- Ekstrand et al. (2018): Exploring author gender in book rating and recommendation
- Ekstrand et al. (2014): User perception of differences in recommender algorithms
- Selbst et al. (2019): Fairness and Abstraction in Sociotechnical Systems
- Pinney et al. (2023): Much Ado About Gender: Current Practices and Future Recommendations for Appropriate Gender-Aware Information Access
- Diaz et al. (2020): Evaluating Stochastic Rankings with Expected Exposure
- Raj et al. (2022): Fire Dragon and Unicorn Princess; Gender Stereotypes and Children's Products in Search Engine Responses
- Mitchell et al. (2021): Algorithmic Fairness: Choices, Assumptions, and Definitions
- Mehrotra et al. (2018): Towards a Fair Marketplace: Counterfactual Evaluation of the trade-off between Relevance, Fairness & Satisfaction in Recommender Systems
- Raj et al. (2022): Measuring Fairness in Ranked Results: An Analytical and Empirical Comparison
- Beutel et al. (2019): Fairness in Recommendation Ranking through Pairwise Comparisons
- Beutel et al. (2017): Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations
- Dwork et al. (2018): Fairness Under Composition
- Bower et al. (2022): Random Isn't Always Fair: Candidate Set Imbalance and Exposure Inequality in Recommender Systems
- Zehlike et al. (2022): Fairness in Ranking: A Survey
- Hoffmann (2019): Where fairness fails: data, algorithms, and the limits of antidiscrimination discourse
- Sweeney (2013): Discrimination in Online Ad Delivery: Google ads, black names and white names, racial discrimination, and click advertising
- Wang et al. (2021): User Fairness, Item Fairness, and Diversity for Rankings in Two-Sided Markets
General Links:
- Follow me on Twitter: https://twitter.com/MarcelKurovski
- Send me your comments, questions and suggestions to marcel@recsperts.com
- Podcast Website: https://www.recsperts.com/
