#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.

  • (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

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#16: Fairness in Recommender Systems with Michael D. Ekstrand
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