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The Behavioral Design Podcast
Recommender Systems with Carey Morewedge
Oct 23, 2024
Carey Morewedge, a Boston University marketing professor and expert in decision-making and AI systems, dives into the mechanics of AI recommender systems. He explains how platforms like Netflix and TikTok utilize user data to enhance engagement, often at the expense of long-term satisfaction. The conversation highlights ethical considerations, including the mismatch between stated and actual preferences, and the risks of over-relying on algorithms. Carey and the hosts discuss the delicate balance of human behavior and algorithmic influence, raising intriguing questions about technology's role in our lives.
59:11
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Quick takeaways
- AI recommender systems utilize content-based and collaborative filtering methods, shaping user experiences by predicting preferences from implicit data.
- Ethical considerations arise from the gap between revealed and normative preferences, highlighting the risks of algorithmic overreliance without contextual understanding.
Deep dives
Navigating Recommender Systems
Navigating digital platforms often involves users actively curating their information feeds to align with their interests while avoiding content they wish to exclude. Users can manipulate algorithms by blocking certain topics or downvoting content they find irrelevant, thus creating a tailored experience that reflects their preferences. This self-management can serve as a commitment device, adding friction to the consumption of content that is not in line with their goals, leading to a more meaningful engagement on platforms like Twitter. However, many users find themselves overwhelmed by irrelevant recommendations and often resort to self-control measures to avoid unwanted distractions.
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