Episode

Social Choice for Fair Recommendations

Podcast
Data Skeptic
Published
Jul 27, 2026
Duration seconds
2576
Processing state
not_requested
Canonical source
https://dataskeptic.com/blog/episodes/2026/social-choice-for-fair-recommendations
Audio
https://pscrb.fm/rss/p/mgln.ai/e/35/traffic.libsyn.com/secure/dataskeptic/Robin_No_Ads_V1.mp3?dest-id=201630
JSON
/v1/public/podcasts/data-skeptic/episodes/social-choice-for-fair-recommendations
Markdown
/podcast/data-skeptic/social-choice-for-fair-recommendations.md

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Summary

Recommender systems influence nearly every aspect of our digital lives—but what does it mean for those systems to be fair? Robin Burke joins Data Skeptic to discuss the history of recommender systems, the limitations of optimizing purely for accuracy, and how ideas from social choice theory can help balance the needs of users, creators, and society. The conversation explores the future of recommendation algorithms and why fairness is a far more complex challenge than it first appears.