Algorithmic fairness research has largely framed unfairness as discrimination along sensitive attributes. However, this approach limits visibility into unfairness as structural injustice instantiated through social determinants, which are contextual variables that shape attributes and outcomes without pertaining to specific individuals. This position paper argues that the field should quantify structural injustice via social determinants, beyond sensitive attributes. Drawing on cross-disciplinary insights, we argue that prevailing technical paradigms fail to adequately capture unfairness as structural injustice, because contexts are potentially treated as noise to be normalized rather than signal to be audited. We further demonstrate the practical urgency of this shift through a theoretical model of college admissions, a demographic study using U.S. census data, and a high-stakes domain application regarding breast cancer screening within an integrated U.S. healthcare system. Our results indicate that mitigation strategies centered solely on sensitive attributes can introduce new forms of structural injustice. We contend that auditing structural injustice through social determinants must precede mitigation, and call for new technical developments that move beyond sensitive-attribute-centered notions of fairness as non-discrimination.

翻译：摘要：算法公平研究在很大程度上将不公平定义为沿敏感属性的歧视。然而，这种方法限制了将不公平视为通过社会决定因素实现的结构性不公正的可见性——这些社会决定因素是塑造属性和结果的背景变量，但不涉及特定个体。本立场论文认为，该领域应通过社会决定因素量化结构性不公正，而非仅仅关注敏感属性。借鉴跨学科见解，我们认为当前主流技术范式未能充分捕捉作为结构性不公正的不公平，因为背景可能被视为需要标准化的噪声，而非需要审计的信号。我们进一步通过大学录取的理论模型、使用美国人口普查数据的人口学研究，以及涉及美国综合医疗系统内乳腺癌筛查的高风险领域应用，证明了这一转变的实践紧迫性。我们的结果表明，仅围绕敏感属性的缓解策略可能引入新形式的结构性不公正。我们主张，必须在缓解措施之前通过社会决定因素审计结构性不公正，并呼吁开展新的技术发展，以超越将公平视为非歧视的、以敏感属性为中心的概念。