FAI: Quantifying Direct and Indirect Consequences of Racial Disparities in Outcomes Following Cardiac Surgery

随着学习算法在我们的生活中变得无处不在，观察者和内部人士都对潜在的有害或歧视性偏见和差异表示担忧。当算法以不适当的方式使用数据中的敏感特征时，例如种族，年龄，性别或性取向，可能会出现这些问题。 令人不安的种族差异已被发现为许多种健康结果。 特别地，已知非裔美国人与其他种族群体相比具有更高的冠心病患病率，并且已知在经历外科手术干预之后遭受更高的术后发病率和死亡率。 虽然这些差异在文献中得到了很好的证实，但它们在多大程度上是由于生物因素、社会经济因素或所提供护理的差异而引起的尚不清楚。该提案将通过因果调解分析和公平意识算法的工具以及从电子健康记录中获得的丰富数据集的组合，解决在评估和解决健康结果差异原因方面的概念，方法和实际差距。这将确保用于医疗保健环境中的预测和决策支持的学习算法的好处公平公正地适用于所有人。此外，作为该项目的一部分，该项目将允许在大学的数据科学课程中引入差异和算法公平性。本研究中开发的量化和解决差异的方法和实践创新对于确保用于医疗保健环境中的预测和决策支持的学习算法的益处公平公正地适用于所有人至关重要。拟议项目中的差异和公平性的观点建立在团队的初步工作基础上，其中公平性约束对应于消失的因果效应沿着某些（特定领域）因果模型中的“不允许”途径。因果建模的正式框架允许团队根据因果路径特定效应（PSE）对公平标准进行数学化（非），这些因果路径特定效应可以从观察到的数据中估计出来，然后作为优化任务的约束条件。该项目将严格证明所提出的框架，精确说明所提出的公平约束的正式化（与以前的建议不同）是如何设计的，以干预不公正的循环。重要的是，我们将在这里借鉴道德哲学和科学哲学的相关文献，因为关键概念-公平，系统不公正，因果解释-几十年来一直是哲学中许多辩论和分析的主题。为了解决以前的工作的局限性，这只允许高质量的解决方案相对简单的参数模型，或需要棘手的方法，如拒绝抽样，该项目开发了新的方法，将使用技术从结构嵌套模型的因果推理和经验似然统计的最大似然框架中重新措辞的问题。 这些方法将更容易可靠地扩展到高维数据，并为预测和策略学习问题提供比以前更高质量的解决方案。 这将使我们评估和满足公平约束的方法适用于医疗保健中发现的复杂数据。最后，该项目将把开发的方法应用于接受心脏手术的患者的数据，并进行初步分析，旨在评估差异在多大程度上归因于与生物学、社会经济地位和护理差异相关的途径。 临床团队将开始验证模型和结果。虽然算法公平性是机器学习社区相当感兴趣的一个话题，但已经探索了多种方法，这个提议在三个方面是独一无二的。 首先，所提出的框架是动机良好的，并提供了一个系统的方法来评估不同的，有时是相互冲突的直觉，以前的建议。 第二，该项目旨在正式打破不公正的循环。 最后，所提出的公平推理方法不是对问题的单一方法的增量扩展，而是借鉴了多个社区的见解，并且可以被视为分析哲学，因果推理，半参数统计，该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准。

As learning algorithms become ubiquitous in our lives, both observers and insiders have expressed concerns about the potentially harmful or discriminatory biases and disparities. These issues may arise when algorithms use sensitive features in the data, such as race, age, gender, or sexual orientation, in inappropriate ways. Troubling racial disparities have been discovered for many kinds of health outcomes. In particular, it is known that African Americans have a higher prevalence of coronary heart disease compared to other ethnic groups, and are known to suffer higher rates of post-operative morbidity and mortality, after undergoing surgical interventions. While these disparities are well established in the literature, the extent to which they are due to biological factors, socioeconomic factors, or differences in offered care is not known. This proposal will address the conceptual, methodological, and practical gaps in assessing and addressing reasons for disparities in health outcomes by a combination of tools from causal mediation analysis and fairness-aware algorithms, and a rich dataset obtained from electronic health records. This will ensure the benefits of learning algorithms used for prediction and decision support in healthcare settings apply fairly and equitably to all. In addition, as part of the project, the project will allow for the introduction of disparities and algorithmic fairness into the data science curriculum at the university. Methodological and practical innovations for quantifying and addressing disparities developed in this research are crucial to make sure the benefits of learning algorithms used for prediction and decision support in healthcare settings apply fairly and equitably to all.The perspective on disparities and fairness in the proposed project builds on the team's preliminary work where fairness constraints correspond to vanishing causal effects along certain (domain-specific) "impermissible" pathways in a causal model. The formal framework of causal modeling has allowed the team to mathematize (un)fairness criteria in terms of causal path-specific effects (PSEs) that can be estimated from observed data, and then imposed as constraints on the optimization task. The project will rigorously justify the proposed framework, making precise how the proposed formalization of fairness constraints (unlike previous proposals) is designed to intervene on cycles of injustice. Importantly we will draw on the relevant literature from moral philosophy and philosophy of science here, since the crucial concepts -- fairness, systemic injustice, causal explanation -- have been the subject of much debate and analysis in philosophy for decades. To address the limitations of prior work, which only allowed high quality solutions for relatively simple parametric models, or entailed intractable methods such as rejection sampling, this project develops novel methodology that will use techniques from structural nested models in causal inference and empirical likelihood in statistics to rephrase the problem in the framework of maximum likelihood. These methods will be easier to reliably scale to high dimensional data, and yield much higher quality solutions to both prediction and policy learning problems than previously possible. This will make our methodology for assessing and satisfying fairness constraints applicable to complex data found in healthcare. Finally, this project will apply the developed methodology to data on patients that have undergone heart surgery, and perform preliminary analyses that aim to assess the extent to which disparities are attributable to pathways associated with biology, socioeconomic status, and differences in care. The clinical team will begin to validate the models and the resulting findings. While algorithmic fairness is a topic of considerable interest to the machine learning community, with multiple approaches already explored, this proposal is unique in three ways. First, the proposed framework is well-motivated, and provides a systematic way to evaluate disparate and sometimes conflicting intuitions that underly previous proposals. Second, the project is designed to break the cycles of injustice in a formal sense. Finally, the proposed approach to fair inference is not an incremental extension of a single method to the problem, but draws on insights from multiple communities, and can be viewed as a novel combination of tools from analytic philosophy, causal inference, semi-parametric statistics, and optimization.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.