Gestures Around the Ear
Data Skeptic
The next evolution of wearable technology may not require new screens or controllers, but new ways to interact with the devices we already wear. Shimon explores how gesture-sensing earbuds could transform virtual reality and wearable computing through intuitive, user-centered interactions that make technology more accessible and adaptable.
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Implicit Interactions
Data Skeptic
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 implicit interaction, “Wizard of Oz” prototyping, and what studying pedestrians, self-driving cars, and even robotic furniture can teach us about designing technology that behaves the way people expect.
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The Lived Informatics Model
Data Skeptic
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 informatics, from fitness trackers and food journals to baby tracking and AI, and explains why abandoning a tracking tool doesn’t necessarily mean it failed.
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Recommender Systems Today and Tomorrow
Data Skeptic
In the final episode of our Recommender Systems season, we explore the growing questions of trust, manipulation, privacy, fairness, sustainability, and user control. From fake reviews and shilling attacks to explainable recommendations and user-selected algorithms, we look at what happens when recommender systems must answer not only for what they recommend, but for the consequences of those choices.
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Recommender Systems Optimization Goals
Data Skeptic
In part two of the Data Skeptic Recommender Systems season finale, Kyle asks a deceptively difficult question: what should recommender systems actually optimize for? Drawing on conversations from across the season, the episode explores engagement, filter bubbles, popularity bias, fairness, human curation, embeddings, and the growing role—and risks—of large language models in shaping what gets recommended to us.
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Recommender Systems Origin Story
Data Skeptic
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.
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Social Choice for Fair Recommendations
Data Skeptic
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.
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News Recommendations
Data Skeptic
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 recommendation systems can balance personalization with diversity and societal impact.
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Give Users the Wheel
Data Skeptic
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 transform platforms like YouTube, TikTok, and news feeds while preserving the strengths of modern recommendation algorithms.
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AutoLike
Data Skeptic
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.
info_outlineIn this episode, host Kyle Polich speaks with Roan Schellingerhout, a fourth-year PhD student at Maastricht University, about explainable multi-stakeholder recommender systems for job recruitment. Roan discusses his research on creating AI-powered job matching systems that balance the needs of multiple stakeholders—job seekers, recruiters, HR professionals, and companies. The conversation explores different types of explanations for job recommendations, including textual, bar chart, and graph-based formats, with findings showing that lay users strongly prefer simple textual explanations over more technical visualizations. Roan shares insights from his "healthy friction" study, which tested whether users could distinguish between real AI-generated explanations and randomly generated ones, revealing that participants often used explanations as information sources rather than decision-making tools.
The discussion delves into the technical architecture behind these systems, including the use of knowledge graphs built from tabular data, inference rules, and large language models to generate human-friendly explanations. Roan explains how his research aims to open the black box of recommender systems, making them more transparent and trustworthy for non-technical users. Looking forward, he discusses ongoing work on automated knowledge graph construction from resumes and job listings, research into fairness considerations around gender and location, and plans for real-world testing with actual job seekers. The episode concludes with Roan's vision for the future: AI systems that support rather than replace human recruiters, making the job search process less grueling while maintaining the essential human judgment that recruitment requires.