Latent bias and the implementation of artificial intelligence in medicine
Latent bias and the implementation of artificial intelligence in medicine
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DOI：
10.1093/jamia/ocaa094
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发表时间：
2020-12-01
影响因子：
6.4
通讯作者：
Lindvall, Charlotta
中科院分区：
文献类型：
--
作者：
DeCamp, Matthew;Lindvall, Charlotta
越来越多的人认识到人工智能（AI）算法中的偏见，这促使人们寻求建立公平的模型，没有偏见。然而，建立公平的模型可能只是挑战的一半。一个看似公平的模型可能直接或间接地涉及我们所说的“潜在偏见”。正如潜在错误通常被描述为复杂系统中“等待发生”的错误一样，潜在偏差也是等待发生的偏差。在这里，我们描述了与AI算法中的偏差相关的3个主要挑战，并提出了几种管理它们的方法。在临床实践中广泛实施AI算法之前，迫切需要解决潜在的偏见。
Increasing recognition of biases in artificial intelligence (AI) algorithms has motivated the quest to build fair models, free of biases. However, building fair models may be only half the challenge. A seemingly fair model could involve, directly or indirectly, what we call "latent biases." Just as latent errors are generally described as errors "waiting to happen" in complex systems, latent biases are biases waiting to happen. Here we describe 3 major challenges related to bias in Al algorithms and propose several ways of managing them. There is an urgent need to address latent biases before the widespread implementation of Al algorithms in clinical practice.