Human-AI Matching: The Limits of Algorithmic Search
Best AI papers explained - A podcast by Enoch H. Kang

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This academic paper "Artificial Intelligence Clones" explores the effectiveness of "AI clones" in matching individuals for various purposes, such as dating or hiring, compared to traditional in-person interactions. The author models personalities as points in a multi-dimensional space and AI clones as noisy approximations of these personalities. The central argument is that while AI platforms offer vastly expanded search capacity, the inherent imperfection of AI representations ultimately limits their utility. A key finding suggests that meeting even a small number of people in person can yield better expected matches than searching an infinite pool via AI clones, especially as personality complexity (dimensions) increases. Furthermore, the paper highlights a potential social stratification where individuals with more readily available personal data for AI training might be systematically favored.