This paper concerns the proxy problem: often machine learning programs utilize seemingly innocuous features as proxies for socially-sensitive attributes, posing various challenges for the creation of ethical algorithms. I argue that to address this problem, we must first settle a prior question of what it means for an algorithm that only has access to seemingly neutral features to be using those features as “proxies” for, and so to be making decisions on the basis of, protected-class features. Borrowing resources from philosophy of mind and language, I argue that the answer depends on whether discrimination against those protected classes explains the algorithm’s selection of individuals. This approach rules out standard theories of proxy discrimination in law and computer science that rely on overly intellectual views of agent intentions or on overly deflationary views that reduce proxy use to statistical correlation. Instead, my theory highlights two distinct ways an algorithm can reason using proxies: either the proxies themselves are meaningfully about the protected classes, highlighting a new kind of intentional content for philosophical theories in mind and language; or the algorithm explicitly represents the protected-class features themselves, and proxy discrimination becomes regular, old, run-of-the-mill discrimination.

本文关注代理问题：机器学习程序通常利用看似无害的特征作为社会敏感属性的代理，为创建道德算法带来了各种挑战。我认为，要解决这个问题，我们必须首先解决一个先行问题，即只能访问看似中性特征的算法将这些特征用作受保护类特征的“代理”，从而根据受保护类特征做出决策意味着什么。借用心灵哲学和语言哲学的资源，我认为答案取决于对那些受保护阶层的歧视是否解释了算法对个体的选择。这种方法排除了法律和计算机科学中代理歧视的标准理论，这些理论依赖于对代理意图的过度理性观点，或者基于将代理使用简化为统计相关性的过度通货紧缩的观点。相反，我的理论强调了算法使用代理进行推理的两种不同方式：要么代理本身对受保护的类有意义，突出了思想和语言中哲学理论的一种新的有意内容;或者算法明确表示受保护类特征本身，代理歧视就变成了常规的、古老的、普通的歧视。