算法的不合理效力

Jens O. Ludwig, S. Mullainathan, Ashesh Rambachan
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引用次数: 0

摘要

我们计算了经济学家感兴趣的多个领域--监管、刑事司法、医疗和教育--的算法干预的社会回报(具体来说,即公共资金的边际价值(MVPF))。尽管这些算法各不相同,但结果却相似且惊人。每种算法的 MVPF 都是无穷大:它不仅能产生巨大的效益,还能提供 "免费午餐"。我们认为这些数字并不意味着这些干预措施一定要扩大规模,而是意味着应该投入更多的研发力量,开发并仔细评估针对政策问题的算法解决方案。
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The Unreasonable Effectiveness of Algorithms
We calculate the social return on algorithmic interventions (specifically, their marginal value of public funds (MVPF)) across multiple domains of interest to economists—regulation, criminal justice, medicine, and education. Though these algorithms are different, the results are similar and striking. Each one has an MVPF of infinity: not only does it produce large benefits, it provides a “free lunch.” We do not take these numbers to mean these interventions ought to be necessarily scaled but rather that much more research and development should be devoted to developing and carefully evaluating algorithmic solutions to policy problems.
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