RI: Medium: Foundations of Recourse Verification in Machine Learning

RI：媒介：机器学习资源验证的基础

• 批准号: 2313105
• 负责人: Tsui-Wei Weng
• 金额: $ 118.29万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2313105&HistoricalAwards=false
• 关键词: RI; Medium; Foundations; Recourse; Verification

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.

机器学习模型现在可以自动做出影响美国数百万人的决策，通过分配预测来决定谁将获得贷款、工作面试或公共服务。构建此类模型的现代方法并没有考虑可操作性，即如何考虑。个人可以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。该项目将促进信贷和就业。开发新方法，以确保模型分配个人可以通过其在特征空间中的行为来改变的预测，这些方法将允许人员构建模型，以保护贷款、雇用和公共服务分配等应用程序的访问权。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的方法，这包括 (1) 限制检测的方法，即识别个人由于可操作性而可能无法更改其特征的特征空间区域。 (2) 特定型号验证，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，即训练一个可以通过特征空间中明确定义的动作集来改变预测的模型。利用研究团队在使用现代优化技术来促进机器学习的公平性、稳健性和可靠性方面的专业知识，并通过借贷和招聘的实际应用进行完善。该奖项反映了 NSF 的法定使命，并被认为值得支持通过评估使用基金会的智力价值和更广泛的影响审查标准。