RI: Medium: Foundations of Recourse Verification in Machine Learning

机器学习模型现在可以自动化影响美国数百万人的决策，分配预测来决定谁将获得贷款，工作面试或公共服务。建立这种模型的现代方法没有考虑到可诉性-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分配固定的预测-这意味着被拒绝贷款或面试的个人可能会被永久锁定在信贷和就业之外。该项目将开发新的方法，以确保模型分配的预测，个人可以通过他们在特征空间中的行动改变。这些方法将使从业者能够构建模型，保护贷款，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现代机器学习生命周期各个阶段的访问权的方法。这包括用于（1）限制检测的方法，即，以识别特征空间的区域，其中个体可能由于可动作性约束而不能改变其特征;（2）模型特定验证，即，检查模型是否可以在模型开发或部署中提供追索权;（3）学习追索权保证，即，训练一个模型，它的预测可以通过特征空间中一组定义良好的动作来改变。该方法将利用研究团队在使用现代优化技术方面的专业知识，以促进机器学习的公平性，鲁棒性和可靠性，并通过贷款和招聘的实际应用进行改进。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值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.