# Data Skeptic > A podcast about data science, machine learning, artificial intelligence, and statistics, hosted by Kyle Polich. Episodes are long-form interviews with researchers and practitioners, published since 2014. Full episode transcripts are published on each episode page. 526 episodes across 15 themed seasons. ## Seasons - [Physically Distributed](https://dataskeptic.com/podcast/physically-distributed): When systems and people are geographically separated, coordination becomes a technical and social challenge. This season investigates how distributed work and… - [Natural Language Processing](https://dataskeptic.com/podcast/natural-language-processing): Teaching computers to understand language is one of the most challenging problems in AI. This season walks through the evolution of natural language… - [Artificial Intelligence](https://dataskeptic.com/podcast/artificial-intelligence): AI is reshaping every industry, but what exactly is it — and what isn’t it? These episodes aim to define artificial intelligence in a rapidly evolving… - [Time Series](https://dataskeptic.com/podcast/time-series): Understanding how data evolves over time is key to everything from forecasting demand to interpreting social trends. This season breaks down the structure and… - [Human-Computer Interaction](https://dataskeptic.com/podcast/human-computer-interaction): Human-computer interaction is the study of how people interact with technology. From interfaces to intent prediction, and from dark patterns to delightful… - [Fake News](https://dataskeptic.com/podcast/fake-news): Misinformation isn't new, but the modern web has given it unprecedented reach and influence. This season investigates how false narratives spread, why they're… - [Ad-tech](https://dataskeptic.com/podcast/ad-tech): Online ads don’t just appear — they’re chosen through rapid-fire auctions, behavioral tracking, and complex optimization algorithms. These episodes reveal the… - [Consensus](https://dataskeptic.com/podcast/consensus): What does it mean to agree? This season explores how consensus is formed, whether among people or machines. Episodes dive into distributed systems protocols… - [Machine Intelligence](https://dataskeptic.com/podcast/machine-intelligence): What makes machines intelligent — and how close are we to achieving it? Through interviews and explainers, this season probes the current state of AI… - [Graphs and Networks](https://dataskeptic.com/podcast/graphs-and-networks): Connections matter — in social media, biology, transportation, and beyond. This season maps out the study of graphs and networks, explaining core concepts… - [All About Surveys](https://dataskeptic.com/podcast/all-about-surveys): Surveys might seem simple, but good data collection requires nuance, rigor, and constant vigilance. This season digs into the science behind asking questions… - [Recommender Systems](https://dataskeptic.com/podcast/recommender-systems): The're not just for eCommerce. Recommender systems have infiltrated our video watching, travel planning, social media feeds, and other daily activities. This… - [Animal Intelligence](https://dataskeptic.com/podcast/animal-intelligence): How smart are animals — and how do we know? With a special co-host joining the show, this season explores cognition across the animal kingdom. Featuring… - [Interpretability](https://dataskeptic.com/podcast/interpretability): As AI grows more powerful, understanding how models make decisions becomes critical. This season explores the tools and frameworks for interpreting machine… - [k-Means Clustering](https://dataskeptic.com/podcast/k-means): Clustering helps us find structure in unlabeled data — and this short season is a deep dive into one of the most popular algorithms: *k*-means. With clear… ## Recent Episodes - [Gestures Around the Ear](https://dataskeptic.com/blog/episodes/2026/gestures-around-the-ear) (2026-10-18): The next evolution of wearable technology may not require new screens or controllers, but new ways to interact with the devices we already wear. Shaikh Shawon… - [Implicit Interactions](https://dataskeptic.com/blog/episodes/2026/implicit-interactions) (2026-10-05): How do we design robots and autonomous vehicles that understand the unwritten rules of human behavior? Kyle speaks with Cornell Tech professor Wendy Ju about… - [The Lived Informatics Model](https://dataskeptic.com/blog/episodes/2026/the-lived-informatics-model) (2026-09-25): The data we collect about ourselves can tell us a lot—but only if the technology collecting it actually fits into our lives. Daniel Epstein explores personal… - [Recommender Systems Today and Tomorrow](https://dataskeptic.com/blog/episodes/2026/recommender-systems-today-and-tomorrow) (2026-09-09): In the final episode of our Recommender Systems season, we explore the growing questions of trust, manipulation, privacy, fairness, sustainability, and user… - [Recommender Systems Optimization Goals](https://dataskeptic.com/blog/episodes/2026/recommender-systems-optimization-goals) (2026-09-01): In part two of the Data Skeptic Recommender Systems season finale, Kyle asks a deceptively difficult question: what should recommender systems actually… - [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… - [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… - [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… - [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… - [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… - [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… - [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, Associate Professor at the Polytechnic University of Bari, to explore how recommender systems have evolved and why… - [Book Ratings and Recommendations](https://dataskeptic.com/blog/episodes/2026/book-ratings-and-recommendations) (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… - [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… - [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… - [Niche vs Mainstream](https://dataskeptic.com/blog/episodes/2026/niche-vs-mainstream) (2026-02-17): Anas Buhayh discusses multi-stakeholder fairness in recommender systems and the S'mores framework—a simulation allowing users to choose between mainstream and… - [Healthy Friction in Job Recommender Systems](https://dataskeptic.com/blog/episodes/2026/healthy-friction-in-job-recommender-systems) (2026-02-02): Host Kyle Polich interviews Roan Schellingerhout, a PhD student researching explainable AI-powered job matching systems that balance the needs of job seekers… - [Fairness in PCA-Based Recommenders](https://dataskeptic.com/blog/episodes/2026/fairness-in-pca-based-recommenders) (2026-01-26): Predicting what someone will want next is harder than it looks. We explore the fascinating world of recommender systems and algorithmic fairness with David… - [Video Recommendations in Industry](https://dataskeptic.com/blog/episodes/2025/video-recommendations-in-industry) (2025-12-26): Machine learning can make impressive predictions while raising equally important questions. Kyle Polich sits down with Cory Zechmann, a content curator… - [Eye Tracking in Recommender Systems](https://dataskeptic.com/blog/episodes/2025/eye-tracking-in-recommender-systems) (2025-12-18): What appears in your feed is rarely there by accident. Santiago de Leon takes us deep into the world of eye tracking and its revolutionary applications in… - [Cracking the Cold Start Problem](https://dataskeptic.com/blog/episodes/2025/cracking-the-cold-start-problem) (2025-12-08): What appears in your feed is rarely there by accident. We dive deep into the technical foundations of building modern recommender systems. Unlike traditional… - [Designing Recommender Systems for Digital Humanities](https://dataskeptic.com/blog/episodes/2025/designing-recommender-systems-for-digital-humanities) (2025-11-23): Recommendation systems shape discovery in ways most users never see. We explore the fascinating intersection of recommender systems and digital humanities… - [DataRec Library for Reproducible in Recommend Systems](https://dataskeptic.com/blog/episodes/2025/datarec-library-for-reproducible-in-recommend-systems) (2025-11-13): Behind every useful recommendation is a series of decisions about preference, relevance, and uncertainty. Host Kyle Polich explores DataRec, a new Python… - [Shilling Attacks on Recommender Systems](https://dataskeptic.com/blog/episodes/2025/shilling-attacks-on-recommender-systems) (2025-11-05): The recommendations that feel effortless are powered by surprisingly complicated choices. Kyle sits down with Aditya Chichani, a senior machine learning… - [Music Playlist Recommendations](https://dataskeptic.com/blog/episodes/2025/music-playlist-recommendations) (2025-10-29): The recommendations that feel effortless are powered by surprisingly complicated choices. Rebecca Salganik, a PhD student at the University of Rochester with… - [Bypassing the Popularity Bias](https://dataskeptic.com/blog/episodes/2025/bypassing-the-popularity-bias) (2025-10-15): Predicting what someone will want next is harder than it looks. We speak with Václav Blahut, a machine learning researcher at Seznam.cz, about tackling… - [Sustainable Recommender Systems for Tourism](https://dataskeptic.com/blog/episodes/2025/sustainable-recommender-systems-for-tourism) (2025-10-09): Behind every useful recommendation is a series of decisions about preference, relevance, and uncertainty. We speak with Ashmi Banerjee, a doctoral candidate… - [Interpretable Real Estate Recommendations](https://dataskeptic.com/blog/episodes/2025/interpretable-real-estate-recommendations) (2025-09-22): Predicting what someone will want next is harder than it looks. Host Kyle Polich interviews Dr. Kunal Mukherjee, a postdoctoral research associate at Virginia… - [Why am I seeing This?](https://dataskeptic.com/blog/episodes/2025/why-am-i-seeing-this) (2025-09-08): The recommendations that feel effortless are powered by surprisingly complicated choices. We explore the challenges of studying social media recommender… - [Eco-aware GNN Recommenders](https://dataskeptic.com/blog/episodes/2025/eco-aware-gnn-recommenders) (2025-08-30): Recommendation systems shape discovery in ways most users never see. We dive into eco-friendly AI with Antonio Purificato, a PhD student from Sapienza… - [Networks and Recommender Systems](https://dataskeptic.com/blog/episodes/2025/networks-and-recommender-systems) (2025-08-17): Kyle reveals the next season's topic will be "Recommender Systems". Asaf shares insights on how network science contributes to the recommender system field - [Network of Past Guests Collaborations](https://dataskeptic.com/blog/episodes/2025/network-of-past-guests-collaborations) (2025-07-21): Kyle and Asaf discuss a project in which we link former guests of the podcast based on their co-authorship of academic papers. - [The Network Diversion Problem ](https://dataskeptic.com/blog/episodes/2025/the-network-diversion-problem) (2025-07-06): Professor Pål Grønås Drange of the University of Bergen introduces **Parameterized Complexity**, a framework for tackling difficult computational problems by… - [Complex Dynamics in Networks](https://dataskeptic.com/blog/episodes/2025/complex-dynamics-in-networks) (2025-06-28): Complex systems often make more sense once we map the relationships inside them. We learn why simply analyzing the structure of a network is not enough, and… - [Github Network Analysis](https://dataskeptic.com/blog/episodes/2025/github-network-analysis) (2025-06-22): Complex systems often make more sense once we map the relationships inside them. We'll discuss how to use Github data as a network to extract insights about… - [Networks and Complexity](https://dataskeptic.com/blog/episodes/2025/networks-and-complexity) (2025-06-14): Complex systems often make more sense once we map the relationships inside them. Kyle does an overview of the intersection of graph theory and computational… - [Actantial Networks](https://dataskeptic.com/blog/episodes/2025/actantial-networks) (2025-06-01): Connections can tell a story that individual observations cannot. Listeners will learn about Actantial Networks—graph-based representations of narratives… - [Graphs for Causal AI](https://dataskeptic.com/blog/episodes/2025/graphs-for-causal-ai) (2025-05-24): Utkarshani Jaimini of the University of South Carolina’s Artificial Intelligence Institute explores how AI can move beyond correlation to better understand… - [Power Networks](https://dataskeptic.com/blog/episodes/2025/power-networks) (2025-05-16): Benjamin Schäfer of the Karlsruhe Institute of Technology explores **Braess’s paradox**—the surprising phenomenon where adding a new connection to a network… - [Unveiling Graph Datasets](https://dataskeptic.com/blog/episodes/2025/unveiling-graph-datasets) (2025-05-08): Bastian Rieck, a tenured professor of machine learning at the University of Fribourg and head of the AIDOS lab, brings a unique perspective combining pure… ## Optional - [All episodes](https://dataskeptic.com/podcast): complete episode archive - [Search](https://dataskeptic.com/search): full-text search across every episode transcript - [About](https://dataskeptic.com/about): about the show and host - [Advertising](https://dataskeptic.com/advertising): sponsorship information - [Sitemap](https://dataskeptic.com/sitemap.xml): machine-readable URL list