The Racist Algorithm
The Racist Algorithm
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种族主义算法
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Anupam Chander
中科院分区：
文献类型：
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作者：
Anupam Chander
我们是否正处于算法种族隔离的边缘？大数据时代是否会导致不公平的决策，使一个种族比其他种族更受青睐，或者使男性比女性更受青睐？在信息时代到来之际，法律的学者们发出了警告，警告人们自动算法的普遍存在越来越多地支配着我们的生活。在他的新书《黑箱社会：金钱和信息背后隐藏的算法》（The Black Box Society：The Hidden Algorithms Behind Money and Information）中，弗兰克·帕斯夸尔（Frank Pasquale）有力地指出，人类越来越依赖计算机化的算法来决定我们收到什么信息，我们可以借多少钱，我们去哪里吃饭，甚至我们和谁约会。帕斯夸尔的核心主张是，这些算法将掩盖令人反感的歧视，破坏民主，加剧不平等。在这篇评论中，我反驳了这一突出的主张。我认为，任何公平的评估算法必须对他们的替代品。算法当然是晦涩和神秘的，但往往并不比它们所取代的委员会或个人更神秘。最终的黑盒子是人类的思想。依靠当代理论的无意识歧视，我表明，有意识的种族主义或性别歧视的算法是不太可能比有意识或无意识的种族主义或性别歧视的人类决策者，它取代。算法歧视的主要问题在于其他地方，在我称之为病毒歧视的过程中：在一个充满歧视效应的世界中训练或运行的算法很可能会复制这种歧视，我认为解决这个问题的办法在于一种算法平权行动。这需要在包含不同社区的数据上训练算法，并不断评估不同影响的结果。这将要求决策者以一种有种族和性别意识的方式来处理算法设计和评估，而不是坚持种族或性别中立和盲目。
Are we on the verge of an apartheid by algorithm? Will the age of big data lead to decisions that unfairly favor one race over others, or men over women? At the dawn of the Information Age, legal scholars are sounding warnings about the ubiquity of automated algorithms that increasingly govern our lives. In his new book, The Black Box Society: The Hidden Algorithms Behind Money and Information, Frank Pasquale forcefully argues that human beings are increasingly relying on computerized algorithms that make decisions about what information we receive, how much we can borrow, where we go for dinner, or even whom we date. Pasquale’s central claim is that these algorithms will mask invidious discrimination, undermining democracy and worsening inequality. In this review, I rebut this prominent claim. I argue that any fair assessment of algorithms must be made against their alternative. Algorithms are certainly obscure and mysterious, but often no more so than the committees or individuals they replace. The ultimate black box is the human mind. Relying on contemporary theories of unconscious discrimination, I show that the consciously racist or sexist algorithm is less likely than the consciously or unconsciously racist or sexist human decision-maker it replaces. The principal problem of algorithmic discrimination lies elsewhere, in a process I label viral discrimination: algorithms trained or operated on a world pervaded by discriminatory effects are likely to reproduce that discrimination.I argue that the solution to this problem lies in a kind of algorithmic affirmative action. This would require training algorithms on data that includes diverse communities and continually assessing the results for disparate impacts. Instead of insisting on race or gender neutrality and blindness, this would require decision-makers to approach algorithmic design and assessment in a race and gender conscious manner.