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.

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.

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.

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.

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.
The episode closes with a look at "agentic" recommender systems, where tools and memory shift recommendations from ranked lists to end-to-end task completion.