The idea of equal opportunity enjoys wide acceptance because of the freedom opportunities provide us to shape our lives. Many disagree deeply, however, about the meaning of equal opportunity, especially in algorithmic decision-making. A new theory of equal opportunity adopts a structural approach, describing how decisions can operate as bottlenecks or narrow places in the structure of opportunities. This viewpoint on discrimination highlights fundamental problems with equal opportunity and its achievement through formal fairness interventions, and instead advocates for a more pluralistic approach that prioritizes opening up more opportunities for more people. We extend this theory of bottlenecks to data-driven decision-making, adapting it to center concerns about the extent to which algorithms can create severe bottlenecks in the opportunity structure. We recommend algorithmic pluralism: the prioritization of alleviating severity in systems of algorithmic decision-making. Drawing on examples from education, healthcare, and criminal justice, we show how this structural approach helps reframe debates about equal opportunity in system design and regulation, and how algorithmic pluralism could help expand opportunities in a more positive-sum way.

翻译：机会均等理念因其赋予我们塑造生活的自由而广受认可。然而，人们对机会均等的含义，特别是在算法决策领域中的含义，存在深刻分歧。一种新的机会均等理论采用结构性方法，描述决策如何在机会结构中充当瓶颈或狭窄通道。这种关于歧视的视角揭示了机会均等及其通过形式公平干预实现过程中的根本问题，转而倡导一种更具多元性的方法，优先为更多人开辟更多机会。我们将这一瓶颈理论扩展至数据驱动决策，使其适应对算法在多大程度上会在机会结构中造成严重瓶颈这一核心关切。我们推荐算法多元主义：优先缓解答算法决策系统严重性的理念。通过教育、医疗和刑事司法等领域的案例，我们展示了这种结构性方法如何帮助重构系统设计与监管中关于机会均等的辩论，以及算法多元主义如何以更积极共赢的方式拓展机会。