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:

• Google Scholar Profile

• Carey Morewedge - LinkedIn

• Boston University Faculty Page

• Personal Website

Understanding AI Recommender Systems:

• How Netflix’s Recommendation System Works
• Implicit Feedback for Recommender Systems (Research Paper)
• Why People Don’t Trust Algorithms (Harvard Business Review)
• ⁠Nuance Behavior Website⁠

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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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