Skip to content
TrackPodcasts
technologyJun 17, 202635:18pending

AutoLike

Data Skeptic

About this episode

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.

Get every episode summarized

Each time Data Skeptic publishes, we email you a written briefing from the transcript — the topics, who appeared, and any specific claims, with the ad reads skipped.

Email me new episodes

Free for 3 shows. No card needed.

Hosts & guests

No transcript yet

This episode has not been transcribed. Request it and it moves to the front of the queue.

AutoLike

Data Skeptic

0:00
35:18

More episodes

More from Data Skeptic

View all episodes →