FAI: Breaking the Tradeoff Barrier in Algorithmic Fairness

为了健壮和值得信赖，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，通过对其训练数据中代表的多数群体进行优化，以牺牲少数群体的较差表现为代价。为了解决这个普遍存在的问题，出现了大量关于“算法公平”的文献。然而，在技术层面上，这篇文献将各种技术上的“公平”概念视为约束，因此通过约束优化的镜头来看待“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，但它导致了权衡成为“公平机器学习”的中心研究对象。在标准框架中，增加新的受保护人口，或在数量上加强公平约束，必然会导致总体和每个组内的准确性下降。这会导致不同利益相关者的利益相互对立，并使人们很难围绕“公平的机器学习”技术达成共识。该项目的总体目标是突破这种“公平/精度权衡”的模式。具体地说，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的误差改善方式来满足。例如，如果发现部署的模型在某些总体上具有不可接受的高误差，我们的目标将是找到在不增加任何其他总体的误差的情况下减少该总体的误差的方法。我们还致力于找到不需要提前确定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，因为这可能非常难以预测。相反，我们将开发方法来动态更新模型，因为发现它们在感兴趣的人群上执行得很差。最后，我们不是抽象地谈论预测模型的“公平性”，而是旨在制定和实现在特定下游应用程序的上下文中具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类型的公平性。除了研究论文和软件，该项目还将通过培训博士生成为值得信赖的机器学习的领先研究人员来开发人力资本。它还将开发针对研究人员、学生和普通公众的教育材料。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.