用因果抽象建模歧视
Modeling Discrimination with Causal Abstraction
AI总结：
本文提出一种因果抽象框架，将种族视为低层社会特征的高层抽象，通过明确陈述社会建构假设来保证模块性，从而在保留精确因果解释的同时厘清歧视建模中的理论分歧。
摘要：
一个人只有在她的种族导致了她受到更差待遇时，才受到直接的种族歧视。这意味着种族是一个与其他属性充分可分离的属性，以便隔离其因果作用。但种族嵌入在一个由社会因素构成的网络中，这些因素抵制被隔离处理。如果种族是社会建构的，那么它在什么意义上能够导致更差待遇？一些人提出，是对种族的感知，而非种族本身，导致了更差待遇。另一些人则认为，由于因果模型需要模块性，即隔离因果效应的能力，因此试图对歧视进行因果建模是误入歧途的。本文以不同的方式处理这一问题。我们引入了一个用于推理歧视的框架，在该框架中，种族是低层特征的高层抽象。在这个框架中，种族可以被建模为自身导致更差待遇。通过允许关于社会建构的假设被精确且明确地陈述——即通过种族与其构成要素之间的对齐——来确保模块性。这些假设随后可以接受规范性和经验性的挑战，从而引出关于歧视何时发生的不同观点。通过区分构成性关系与因果关系，抽象框架精确指出了当前关于歧视建模文献中的分歧，同时保留了对歧视的精确因果解释。
英文摘要：
A person is directly racially discriminated against only if her race caused her worse treatment. This implies that race is an attribute sufficiently separable from other attributes to isolate its causal role. But race is embedded in a nexus of social factors that resist isolated treatment. If race is socially constructed, in what sense can it cause worse treatment? Some propose that the perception of race, rather than race itself, causes worse treatment. Others suggest that since causal models require \textit{modularity}, i.e. the ability to isolate causal effects, attempts to causally model discrimination are misguided. This paper addresses the problem differently. We introduce a framework for reasoning about discrimination, in which race is a high-level \textit{abstraction} of lower-level features. In this framework, race can be modeled as itself causing worse treatment. Modularity is ensured by allowing assumptions about social construction to be precisely and explicitly stated, via an alignment between race and its constituents. Such assumptions can then be subjected to normative and empirical challenges, which lead to different views of when discrimination occurs. By distinguishing constitutive and causal relations, the abstraction framework pinpoints disagreements in the current literature on modeling discrimination, while preserving a precise causal account of discrimination.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
