RI: Medium: Foundations of Recourse Verification in Machine Learning

机器学习模型现在自动化了影响美国数百万人的决策，通过预测来决定谁将获得贷款、工作面试或公共服务。构建这类模型的现代方法没有考虑可操作性--即个人如何修改模型使用的特征来确定他们的预测。因此，贷款和招聘等领域的模型可以给出固定的预测--这意味着被拒绝贷款或面试的个人可能永远无法获得信贷和就业机会。这个项目将开发新的方法来确保模型分配预测，这些预测可以通过个人在特征空间中的操作来改变。这些方法将允许从业者建立模型，保护借贷、招聘和公共服务分配等应用程序的访问权。该项目将开发可用于确保在现代机器学习生命周期的不同阶段访问的方法。这包括用于(1)限制检测的方法，即，识别特征空间中个体可能由于可操作性限制而无法改变其特征的区域；(2)特定于模型的验证，即，检查模型是否可以在模型开发或部署中提供资源；(3)具有资源保证的学习，即，训练其预测可以通过特征空间中的一组明确定义的动作来改变的模型。这些方法将利用研究团队在使用现代优化技术提高机器学习中的公平性、稳健性和可靠性方面的专业知识，并通过在借贷和租用方面的真实应用进行改进。该奖项反映了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。

Machine learning models now automate decisions that affect millions of individuals in the United States, assigning predictions to decide who will receive a loan, a job interview, or a public service. Modern approaches for building such models do not account for actionability - i.e., how individuals can modify the features used by a model to determine their predictions. As a result, models in domains like lending and hiring can assign predictions that are fixed - meaning that individuals who are denied a loan or an interview may be permanently locked out from access to credit and employment. This project will develop new methods to ensure that models assign predictions that individuals can change through their actions in feature space. These methods will allow practitioners to build models that protect the right to access in applications like lending, hiring, and the allocation of public services.The project will develop methods that can be used to ensure access at various stages of the modern machine learning lifecycle. This includes methods for (1) confinement detection, i.e., to identify regions of feature space where individuals may be unable to change their features due to actionability constraints; (2) model-specific verification, i.e., to check that a model can provide recourse in model development or deployment; (3) learning with recourse guarantees, i.e., to train a model whose predictions can be changed through a well-defined set of actions in feature space. The methods will draw on the research teams’ expertise in using modern optimization techniques to promote fairness, robustness, and reliability in machine learning, and be refined through real-world applications in lending and hiring.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.