FAI: Breaking the Tradeoff Barrier in Algorithmic Fairness

为了具有鲁棒性和可信赖性，算法系统需要有效地服务于不同的用户群体。标准的机器学习方法在这方面很容易失败，例如，通过优化训练数据中表示的多数人群，以牺牲少数群体的表现为代价。为了解决这个普遍存在的问题，出现了大量关于“算法公平性”的文献。然而，在技术层面上，这些文献将“公平”的各种技术概念视为约束，因此通过约束优化的视角来看待“公平学习”。虽然从算法设计的角度来看，这是一个富有成效的观点，但它导致权衡成为“公平机器学习”研究的中心对象。在标准框架中，增加新的受保护人群，或在数量上加强公平约束，必然导致总体上和每个群体内部准确性的降低。这会使不同利益相关者的利益相互对立，并使围绕“公平的机器学习”技术达成共识变得困难。这个项目的首要目标是打破这种“公平性/准确性权衡”的模式。具体来说，我们将借鉴学习理论和不确定性估计的思想来引入公平的概念，这些概念可以通过单调误差改进的方式来满足。例如，如果发现部署的模型在某些种群上具有高得令人无法接受的误差，我们的目标将是找到减少该种群上的误差而不增加其他种群上的误差的方法。我们还希望找到不需要提前识别哪些群体可能因机器学习的特定应用而处于不利地位的方法，因为这很难预测。相反，我们将开发动态更新模型的方法，因为我们发现它们在感兴趣的群体上表现不佳。最后，与其抽象地讨论预测模型的“公平性”，我们的目标是制定和实现在特定下游应用程序上下文中有意义的公平性概念，并找到训练上游预测方法的方法，当预测模型部署在这些下游用例中时，这些方法将保证这些公平性。除了研究论文和软件之外，该项目还将通过培养博士生成为值得信赖的机器学习领域的主要研究人员来开发人力资本。它还将开发针对研究人员、学生和一般公众的教育材料。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

In order to be robust and trustworthy, algorithmic systems need to usefully serve diverse populations of users. Standard machine learning methods can easily fail in this regard, e.g. by optimizing for majority populations represented within their training data at the expense of worse performance on minority populations. A large literature on "algorithmic fairness" has arisen to address this widespread problem. However, at a technical level, this literature has viewed various technical notions of "fairness" as constraints, and has therefore viewed "fair learning" through the lens of constrained optimization. Although this has been a productive viewpoint from the perspective of algorithm design, it has led to tradeoffs being centered as the central object of study in "fair machine learning". In the standard framing, adding new protected populations, or quantitatively strengthening fairness constraints, necessarily leads to decreased accuracy overall and within each group. This has the effect of pitting the interests of different stakeholders against one another, and making it difficult to build consensus around "fair machine learning" techniques. The over-arching goal of this project is to break through this "fairness/accuracy tradeoff" paradigm. Specifically, we will draw on ideas from learning theory and uncertainty estimation to introduce notions of fairness that can be satisfied in ways that are monotonically error improving. For example, if it is discovered that a deployed model has error that is unacceptably high on some population, our aim will be to find ways to decrease the error on that population without increasing the error on any other population. We also aim to find methods that do not require identifying which groups might be disadvantaged by a particular application of machine learning ahead of time, since this can be very hard to predict. Instead, we will develop methods to dynamically update models as it is discovered that they are performing poorly on populations of interest. Finally, rather than talking about "fairness" of predictive models in the abstract, we will aim to formulate and implement notions of fairness that have meaning in the context of particular downstream applications, and find methods of training upstream predictive methods that will guarantee these kinds of fairness when the predictive models are deployed in these downstream use case. In addition to research papers and software, this project will develop human capital by training PhD students to be leading researchers in trustworthy machine learning. It will also develop educational materials aimed at researchers, students, and the general public.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.