While the idea of equal opportunity enjoys a broad consensus, many disagree about what it means for opportunities to be equal. The algorithmic fairness community often relies on formal approaches to quantitatively determine if opportunities are allocated equally. A more structural approach put forth by Joseph Fishkin focuses on the wider network of decisions that determine which opportunities are allocated to whom. In this so-called opportunity structure, decision points represent bottlenecks that are often chained together so that the output of one decision is an input to the next. By evaluating the severity and legitimacy of these bottlenecks, Fishkin offers a qualitative framework to assess whether equal opportunity is infringed upon in a structural way. We adopt this structural viewpoint and use it to reframe many interdisciplinary discussions about equal opportunity in systems of algorithmic decision-making. Drawing on examples from education, healthcare, and criminal justice, we recommend prioritizing regulatory and design-based interventions that alleviate severe bottlenecks in order to help expand access to opportunities in a pluralistic way.

翻译：尽管机会均等的理念获得了广泛共识，但对其内涵的理解仍存在诸多分歧。算法公平领域通常依赖形式化方法来定量判断机会是否被公平分配。约瑟夫·菲什金提出了一种更具结构性的路径，聚焦于决定机会分配对象的决策网络。在这种所谓的"机会结构"中，决策节点代表着常被串联的瓶颈环节——前一个决策的输出成为后一个决策的输入。通过评估这些瓶颈的严峻性与合法性，菲什金构建了一个定性分析框架，用以判断机会均等是否在结构性层面受到侵害。我们采纳这一结构性视角，并以此重新审视算法决策系统中关于机会均等的跨学科讨论。基于教育、医疗及刑事司法领域的案例，我们建议优先采用缓解关键瓶颈的监管与设计干预措施，以通过多元主义路径帮助扩大机会获取渠道。