# Data Skeptic The Data Skeptic Podcast features interviews and discussion of topics related to data science, statistics, machine learning, artificial intelligence and the like, all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches. Page: Feed: --- ## [Recommender Systems Origin Story](https://dataskeptic.com/blog/episodes/2026/recommender-systems-origin-story) _2026-08-18_ Where did recommender systems come from, and how do we know when they're actually working? In part one of Data Skeptic's three-part Recommender Systems finale, Kyle traces the field from collaborative filtering and the Netflix Prize to matrix factorization and modern approaches, while exploring why accuracy alone can't capture what makes a recommendation useful, surprising, or meaningful. [Listen](https://pscrb.fm/rss/p/mgln.ai/e/35/traffic.libsyn.com/secure/dataskeptic/recsys-finale-pt1.mp3?dest-id=201630) ## [Social Choice for Fair Recommendations](https://dataskeptic.com/blog/episodes/2026/social-choice-for-fair-recommendations) _2026-07-27_ 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… [Listen](https://pscrb.fm/rss/p/mgln.ai/e/35/traffic.libsyn.com/secure/dataskeptic/Robin_No_Ads_V1.mp3?dest-id=201630) ## [News Recommendations](https://dataskeptic.com/blog/episodes/2026/news-recommendations) _2026-07-02_ News recommendation algorithms influence far more than what stories we click—they can shape our understanding of the world. In this episode, Kyle Polich speaks with Andreea Iana about responsible AI, filter bubbles, multilingual news recommendation, and her open-source NewsRecLib framework for evaluating recommender systems. They explore why bigger models aren't always better and how future… [Listen](https://pscrb.fm/rss/p/mgln.ai/e/35/traffic.libsyn.com/secure/dataskeptic/Andreea_No_Ads_V1.mp3?dest-id=201630) ## [Give Users the Wheel](https://dataskeptic.com/blog/episodes/2026/give-users-the-wheel) _2026-06-23_ What if you could simply tell a recommendation system what you want instead of relying on likes, dislikes, and watch history? Kyle Polich talks with Fuyuan Lyu about the DPR framework, which combines large language models and traditional recommender systems to give users direct control over recommendations through natural language. Together they explore how conversational interfaces could… [Listen](https://pscrb.fm/rss/p/mgln.ai/e/35/traffic.libsyn.com/secure/dataskeptic/Fuyuan_Lyu_No_Ads_V1.mp3?dest-id=201630) ## [AutoLike](https://dataskeptic.com/blog/episodes/2026/autolike) _2026-06-17_ How can researchers audit recommendation systems when the algorithms are hidden from view? Hieu Le joins Kyle Polich to discuss Auto-Like, a reinforcement learning framework that systematically explores how platforms like TikTok personalize content feeds. The conversation covers recommendation transparency, black-box auditing, and the future of platform accountability. [Listen](https://pscrb.fm/rss/p/mgln.ai/e/35/traffic.libsyn.com/secure/dataskeptic/Hieu_No_Ads_V1.mp3?dest-id=201630) ## [Student Spotlight: Aaron Payne, Data Analyst](https://dataskeptic.com/blog/episodes/2026/student-spotlight-aaron-payne-data-analyst) _2026-05-01_ Aaron Payne, an MBA student at Georgia Tech studying business analytics and a Senior Insights Analyst at Chick-fil-A, joins Kyle Polich to talk about turning analytics into decisions that matter. They unpack a real-world forecasting project with Comfama in Colombia, including messy data realities, interpretability tradeoffs, and why "data science for good" starts with the people impacted. [Listen](https://pscrb.fm/rss/p/mgln.ai/e/35/traffic.libsyn.com/secure/dataskeptic/Aaron_No_Ads_V3.mp3?dest-id=201630) ## [The Future is Agentic in Recommender Systems](https://dataskeptic.com/blog/episodes/2026/The-future-is-agentic-in-recommender-systems) _2026-04-25_ Kyle Polich sits down with Yashar Deldjoo, research scientist and Associate Professor at the Polytechnic University of Bari, to explore how recommender systems have evolved and why trustworthiness matters. They unpack key dimensions of responsible AI, including robustness to adversarial attacks, privacy, explainability, and fairness, and discuss how LLMs introduce new risks like hallucinations.… [Listen](https://pscrb.fm/rss/p/mgln.ai/e/35/traffic.libsyn.com/secure/dataskeptic/Yashar_No_Ads_V1.mp3?dest-id=201630) ## [Book Ratings and Recommendations](https://dataskeptic.com/blog/episodes/2026/book-ratings-and-recomendations) _2026-03-27_ Goodreads star ratings can be misleading as measures of "book quality," and research from Hannes Rosenbusch suggests that for many professionally published books, differences between readers often matter more than differences between books. The episode also explores how to model reader preferences, why reviews often reveal more about the reviewer than the text, and how LLMs can aid computational… [Listen](https://pscrb.fm/rss/p/mgln.ai/e/35/traffic.libsyn.com/secure/dataskeptic/Hannes_No_Ads_V1.mp3?dest-id=201630) ## [Disentanglement and Interpretability in Recommender Systems](https://dataskeptic.com/blog/episodes/2026/disentanglement-and-interpretability-in-recommender-systems) _2026-03-10_ Ervin Dervishaj, a PhD student at the University of Copenhagen, discusses his research on disentangled representation learning in recommender systems, finding that while disentanglement strongly correlates with interpretability, it doesn't consistently improve recommendation performance. The conversation explores how disentanglement acts as a regularizer that can enhance user trust and… [Listen](https://pscrb.fm/rss/p/mgln.ai/e/35/traffic.libsyn.com/secure/dataskeptic/Ervin_No_Ads_V1.mp3?dest-id=201630) ## [Daily plans, scheduled by AI (Sponsored)](https://crawlproof.com/a/0NmeqyZVAwZi) _2026-03-10 · **Sponsored**_ AI-made daily plans that schedule tasks and protect focus time. ## [Collective Altruism in Recommender Systems](https://dataskeptic.com/blog/episodes/2026/collective-altruism-in-recommender-systems) _2026-02-27_ Ekaterina (Kat) Fedorova from MIT EECS joins us to discuss strategic learning in recommender systems—what happens when users collectively coordinate to game recommendation algorithms. Kat's research reveals surprising findings: algorithmic "protest movements" can paradoxically help platforms by providing clearer preference signals, and the challenge of distinguishing coordinated behavior from bot… [Listen](https://pscrb.fm/rss/p/mgln.ai/e/35/traffic.libsyn.com/secure/dataskeptic/Ekatrina_No_With_Ads_V1.mp3?dest-id=201630)