Episode

News Recommendations

Podcast
Data Skeptic
Published
Jul 2, 2026
Duration seconds
2766
Processing state
not_requested
Canonical source
https://dataskeptic.com/blog/episodes/2026/news-recommendations
Audio
https://pscrb.fm/rss/p/mgln.ai/e/35/traffic.libsyn.com/secure/dataskeptic/Andreea_No_Ads_V1.mp3?dest-id=201630
JSON
/v1/public/podcasts/data-skeptic/episodes/news-recommendations
Markdown
/podcast/data-skeptic/news-recommendations.md

Actions

  • POST https://stenobird.com/v1/public/podcasts/data-skeptic/episodes/news-recommendations/transcription-requests
    Idempotently request low-priority transcript generation for this episode.
  • GET https://stenobird.com/podcast/data-skeptic/news-recommendations.md
    Read the agent-friendly Markdown representation of this episode resource.

Summary

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.