
AutoLike
Data Skeptic2026年6月18日35分
AutoLike
Data Skeptic
0:0035:18
このエピソードはアーカイブのため、日本語要約の対象外です。
番組の概要欄(原文)
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.