In this episode of the Behavioral Design Podcast, we delve into the world of AI recommender systems with special guest Carey Morewedge, a leading expert in behavioral science and AI.
The discussion covers the fundamental mechanics behind AI recommendation systems, including content-based filtering, collaborative filtering, and hybrid models. Carey explains how platforms like Netflix, Twitter, and TikTok use implicit data to make predictions about user preferences, and how these systems often prioritize short-term engagement over long-term satisfaction.
The episode also touches on ethical concerns, such as the gap between revealed and normative preferences, and the risks of relying too much on algorithms without considering the full context of human behavior.
Join co-hosts Aline Holzwarth and Samuel Salzer as they together with Carey explore the delicate balance between human preferences and algorithmic influence. This episode is a must-listen for anyone interested in understanding the complexities of AI-driven recommendations!
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LINKS:
Carey Morewedge:
Understanding AI Recommender Systems:
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TIMESTAMPS:
00:00 The 'Do But Not Recommend' Game
07:53 The Complexity of Recommender Systems
08:58 Types of Recommender Systems
12:08 Introducing Carey Morewedge
14:13 Understanding Decision Making in AI
17:00 Challenges in AI Recommendations
32:13 Long-Term Impact on User Behavior
33:00 Understanding User Preferences
35:03 Challenges with A/B Testing
40:06 Algorithm Aversion
46:51 Quickfire Round: To AI or Not to AI
52:55 The Future of AI and Human Relationships
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The song used is Murgatroyd by David Pizarro
