CAREER: Human-Centered Machine Learning: Robustness, Fairness and Dynamics

机器学习（ML）越来越多地用于对人们的机会和福祉产生深远影响的领域，包括医疗保健，执法和消费金融。强调人类在开发机器学习系统中的作用提出了重大挑战。有几个原因是，在许多情况下，机器学习模型训练的数据是随着时间的推移从重复的人类-ML交互中生成的。 例如，在贷款申请决策系统支持系统中，在初始拒绝之后，用户可以改变他们的金融行为以在未来的申请中获得更好的结果。响应于ML决策的人类行为变化改变了ML模型在训练中暴露的数据分布。 然而，训练和使用ML系统的通常范例通常假设静态数据分布。 这要求我们重新审视现有的机器学习工具，以及我们对它们已建立的鲁棒性和公平性的理解。该项目对鲁棒性、公平性和人机交互动态的关注将提醒机器学习从业者盲目信任现有训练数据可能造成的不可挽回的伤害。该项目及其未来的扩展将提供框架和工具，以构建健康的机器学习开发和部署方法，从而更好地为人类的长期福祉服务。该项目旨在提供基本的理解和算法解决方案，以提高以人为中心的机器学习系统中的模型鲁棒性和公平性。该项目建立了理论和计算框架，以了解机器学习模型部署引起的数据变化。我们的智力研究的核心是，在以人为中心的系统中，人类是“响应”的代理人，他们将对算法决策或交互环境做出反应（例如，数据收集系统）。这些反应通常会导致数据分布在训练和部署之间发生变化，这极大地挑战了通常假设的训练数据代表测试数据。该研究结果将有助于推动最先进的技术，为设计强大而公平的机器学习解决方案提供理论上合理且计算效率高的框架，该解决方案考虑了人类对现实世界部署中算法的反应所引发的分布变化。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 (ML) is increasingly used in domains that have a profound effect on people's opportunities and well-being, including healthcare, law enforcement, and consumer finance. The emphasis on the role of humans in developing a machine learning system raises significant challenges. Several arise because the data a machine learning model is trained on is, in many contexts, generated from repeated human-ML interactions over time. For example, in a loan-application decision system-support system, after an initial decline a user may change their financial behavior to obtain a better result in future applications. Human behavior changes in response to the ML decisions change the distribution of data that the ML model is exposed to in training. However the usual paradigms for training and use of ML systems often assumes static data distributions. This requires us to revisit existing machine learning tools and our understanding of their established robustness and fairness properties. This project’s focus on robustness, fairness, and human-ML interaction dynamics will alert machine learning practitioners of the irreparable harm that may be caused by blindly trusting existing training data. The project and its future extensions will provide frameworks and tools to build healthy machine learning development and deployment approaches that will much better serve humans in their long-term well-being. This project aims to provide fundamental understandings and algorithmic solutions to improving model robustness and fairness in a human-centered machine learning system. The project builds theoretical and computational frameworks to understand the changes in data induced by the deployment of machine learning models. Central to our intellectual inquiry is that in human-centered systems, humans are "responding" agents who will react to the algorithmic decisions or the interactive environments (e.g., a data collection system) they are subject to. These reactions often cause data distribution to shift between training and deployment, which substantially challenges the commonly made assumption that the training data represents the test ones. The results will help advance the state-of-the-art by providing a theoretically sound and computationally efficient framework for designing robust and fair machine learning solutions that consider distribution shifts triggered by human responses to the algorithms in real-world deployment.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.