{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Recsperts - Recommender Systems Experts","title":"#16: Fairness in Recommender Systems with Michael D. Ekstrand","html":"","width":"100%","height":180,"duration":6163,"description":"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: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...","thumbnail_url":"https://img.transistorcdn.com/5aEuXaIPxTFiKrepOVeTtB4JSWf3YlVUbAQdiV2LNzA/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9zaG93/LzI0Mzc3LzE2MzIz/NDExMjMtYXJ0d29y/ay5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}