CAREER: Towards Long-term Fairness in Sequential Decision Making

该奖项的全部或部分资金来自2021年美国救援计划法案(公法117-2)公平的机器学习，这是一个旨在减少机器自动化决策中的歧视和偏见的研究课题，是使社会能够广泛接受大规模部署人工智能系统(包括自动决策系统)的关键之一。目前，大多数关于公平机器学习的研究都是基于静态设置的，即机器模型在部署后只为每个人做出一次决定。然而，在实际情况中，机器学习模型通常会被部署来在一段时间内做出顺序决策。在这样的顺序决策环境中，确保每一步的公平性并不能保证长期的公平性，这向公平的机器学习社区提出了一个关于实现长期公平性的具有挑战性和紧迫性的问题。该项目将通过极大地促进对动态环境中公平的基本问题的理解，揭示解决不一致的公平概念之间的冲突的途径，并有助于解决长期公平机器学习的有限的知识基础，从而对公平机器学习产生革命性的变化，这对许多现实世界的应用来说是必不可少的。该教育计划将涉及本科生、毕业生和高中生，以提高他们在解决机器学习和人工智能问题方面的知识和技能，并吸引学生，特别是那些来自代表不足的群体的学生在STEM追求职业生涯。该项目将通过利用珀尔的结构因果模型，为长期公平的机器学习奠定基础。研究人员将重点放在连续决策环境中，过去所做的决定可能会对未来的数据产生影响。将利用软干预来捕捉决策模型部署的因果影响。研究者将基于因果模型建立长期公平性的通用公式，以便通过因果推理技术来衡量长期公平性。然后，根据决策者是否能够获得足够的历史数据，调查者将通过三个渐进的研究任务来研究线下和在线学习环境：(1)研究在给定足够的历史训练数据的情况下实现长期公平的策略和算法；(2)研究如何不仅捕捉历史中的动态，而且预测未来的数据，以便所建立的决策模型在可预测的未来是公平的；(3)转向在线学习，其中决策者几乎没有训练数据，但可以在线方式更新决策模型，并希望最终实现公平。最后，将研究两个现实的扩展，包括无法识别的情况和半马尔科夫模型。这一奖项反映了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。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2)Fair machine learning, a research topic that aims to reduce discrimination and bias in machine-automated decisions, is one of the keys for enabling broad societal acceptance of large-scale deployments of AI systems including automated decision-making systems. Currently the majority of studies in fair machine learning are based on static settings where the machine model makes the decision only once for each individual after its deployment. However, in practical situations, the machine learning model will usually be deployed to make sequential decisions over a period of time. In such sequential decision-making settings, ensuring fairness for each single step does not guarantee fairness in the long-term, presenting a challenging and urgent problem to the fair machine learning community about achieving long-term fairness. This project will make a transformative change to fair machine learning by greatly advancing the understanding of fundamental issues of fairness in dynamic settings, shedding light on the path to addressing conflicts between inconsistent fairness concepts, and contributing to the limited base of knowledge in long-term fair machine learning which is imperative for many real-world applications. The education program will involve undergraduates, graduates and high school students to enhance their knowledge and skills in solving problems in machine learning and artificial intelligence, and attract students especially those from underrepresented groups to pursue careers in STEM.This project will set up the foundation for long-term fair machine learning by leveraging Pearl's Structural Causal Model. The investigator will focus on the sequential decision-making setting where decisions made in the past may have an impact on future data. Soft intervention will be utilized to capture the causal effect of the deployment of decision models. The investigator will develop universal formulations for the long-term fairness based on the causal model so that it can be measured by causal inference techniques. Then, depending on whether the decision maker has access to adequate historical data, the investigator will study both offline and online learning settings via three progressive research tasks: (1) to study strategies and algorithms for achieving long-term fairness given sufficient historical training data; (2) to study how to not only capture the dynamics in the history but also predict the data in the future so that the decision model built would be fair in the predictable future; and (3) to move on to online learning where the decision maker has few or no training data but could update the decision model in an online manner and wants to achieve fairness eventually. Finally, two realistic extensions including unidentifiable situations and semi-Markovian models will be studied.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.