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

机器学习（ML）越来越多地应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。一些问题的出现是因为在许多情况下，机器学习模型所训练的数据是由长期重复的人机交互产生的。例如，在贷款申请决策系统支持系统中，用户在初次拒绝后可能会改变其金融行为，以便在未来的申请中获得更好的结果。人类的行为会随着ML的决策而改变，从而改变ML模型在训练中接触到的数据分布。然而，训练和使用机器学习系统的通常范例通常假设静态数据分布。这需要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属性的理解。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，盲目相信现有的训练数据可能会造成不可弥补的伤害。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，以理解由机器学习模型部署引起的数据变化。我们智力探索的核心是，在以人为中心的系统中，人类是“响应”的代理，他们将对算法决策或他们所处的交互环境（例如，数据收集系统）做出反应。这些反应经常导致数据分布在训练和部署之间发生变化，这实质上挑战了通常假设的训练数据代表测试数据。研究结果将为设计健壮和公平的机器学习解决方案提供一个理论健全和计算高效的框架，从而帮助推进最先进的技术，这些解决方案考虑了人类对现实世界中部署的算法的反应所引发的分布变化。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识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.