1 hr 42 min
#16: Fairness in Recommender Systems with Michael D. Ekstrand Recsperts - Recommender Systems Experts
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- Technology
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.
(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 LinkedInMichael Ekstrand on MastodonMichael's WebsiteGroupLens Lab at University of MinnesotaPeople and Information Research Team (PIReT)6th FAccTRec Workshop: Responsible RecommendationNORMalize: The First Workshop on Normative Design and Evaluation of Recommender SystemsACM Conference on Fairness, Accountability, and Transparency (ACM FAccT)Coursera: Recommender Systems SpecializationLensKit: Python Tools for Recommender SystemsChris Anderson - The Long Tail: Why the Future of Business Is Selling Less of MoreFairness in Recommender Systems (in Recommender Systems Handbook)Ekstrand et al. (2022): Fairness in Information Access SystemsKeynote at EvalRS (CIKM 2022): Do You Want To Hunt A Kraken? Mapping and Expanding Recommendation FairnessFriedler et al. (2021): The (Im)possibility of Fairness: Different Value Systems Require Different Mechanisms For Fair Decision MakingSafiya Umoja Noble (2018): Algorithms of Oppression: How Search Engines Reinforce RacismPapers:
Ekstrand et al. (2018): Exploring author gender in book rating and recommendationEkstrand et al. (2014): User perception of differences in recommender algorithmsSelbst et al. (2019): Fairness and Abstraction in Sociotechnical SystemsPinney et al. (2023): Much Ado About Gender: Current Practices and Future Recommendations for Appropriate Gender-Aware Information AccessDiaz et al. (2020): Evaluating Stochastic Rankings with Expected ExposureRaj et al. (2022): Fire Dragon and Unicorn Princess; Gender Stereotypes and Children's Products in Search Engine ResponsesMitchell et al. (2021): Algorithmic Fairness: Choices, Assumptions, and DefinitionsMehrotra et al. (2018): Towards a Fair Marketplace: Counterfactual Evaluation of the trade-off between Relevance, Fairness & Satisfaction in Recommender SystemsRaj et al. (2022): Measuring Fairness in Ranked Results: An Analytical and Empirical ComparisonBeutel et al. (2019): Fairness in Recommendation Ranking through Pairwise ComparisonsBeutel et al. (2017): Data Decisions and Theoretical Implications when Adversarially Learning Fair RepresentationsDwork et al. (2018): Fairness Under CompositionBower et al. (2022): Random Isn't Always Fair: Candidate Set Imbalance and Exposure Inequality in Recommender SystemsZehlike et al. (2022): Fairness in Ranking: A SurveyHoffmann (2019): Where fairness fails: data, algorit
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.
(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 LinkedInMichael Ekstrand on MastodonMichael's WebsiteGroupLens Lab at University of MinnesotaPeople and Information Research Team (PIReT)6th FAccTRec Workshop: Responsible RecommendationNORMalize: The First Workshop on Normative Design and Evaluation of Recommender SystemsACM Conference on Fairness, Accountability, and Transparency (ACM FAccT)Coursera: Recommender Systems SpecializationLensKit: Python Tools for Recommender SystemsChris Anderson - The Long Tail: Why the Future of Business Is Selling Less of MoreFairness in Recommender Systems (in Recommender Systems Handbook)Ekstrand et al. (2022): Fairness in Information Access SystemsKeynote at EvalRS (CIKM 2022): Do You Want To Hunt A Kraken? Mapping and Expanding Recommendation FairnessFriedler et al. (2021): The (Im)possibility of Fairness: Different Value Systems Require Different Mechanisms For Fair Decision MakingSafiya Umoja Noble (2018): Algorithms of Oppression: How Search Engines Reinforce RacismPapers:
Ekstrand et al. (2018): Exploring author gender in book rating and recommendationEkstrand et al. (2014): User perception of differences in recommender algorithmsSelbst et al. (2019): Fairness and Abstraction in Sociotechnical SystemsPinney et al. (2023): Much Ado About Gender: Current Practices and Future Recommendations for Appropriate Gender-Aware Information AccessDiaz et al. (2020): Evaluating Stochastic Rankings with Expected ExposureRaj et al. (2022): Fire Dragon and Unicorn Princess; Gender Stereotypes and Children's Products in Search Engine ResponsesMitchell et al. (2021): Algorithmic Fairness: Choices, Assumptions, and DefinitionsMehrotra et al. (2018): Towards a Fair Marketplace: Counterfactual Evaluation of the trade-off between Relevance, Fairness & Satisfaction in Recommender SystemsRaj et al. (2022): Measuring Fairness in Ranked Results: An Analytical and Empirical ComparisonBeutel et al. (2019): Fairness in Recommendation Ranking through Pairwise ComparisonsBeutel et al. (2017): Data Decisions and Theoretical Implications when Adversarially Learning Fair RepresentationsDwork et al. (2018): Fairness Under CompositionBower et al. (2022): Random Isn't Always Fair: Candidate Set Imbalance and Exposure Inequality in Recommender SystemsZehlike et al. (2022): Fairness in Ranking: A SurveyHoffmann (2019): Where fairness fails: data, algorit
1 hr 42 min