FAI: Fairness in Machine Learning with Human in the Loop

尽管取得了早期的成功和巨大的潜力，但算法决策系统经常继承和编码训练数据和/或训练过程中存在的偏差。因此，重要的是要了解部署和使用机器学习模型的后果，并提供算法处理，以确保这种技术最终将服务于社会利益。虽然最近的工作已经研究了人工智能中关于“短期”措施的公平问题，但自动化决策的长期后果和影响仍不清楚。对公平决策的长期影响的理解为决策者在动态环境中部署算法模型提供了指导方针，并对其可信度和采用率至关重要。它还将推动算法的设计，着眼于这些算法的制作者和用户的福利，最终目标是实现更公平的结果。该项目旨在通过建立一个分析、算法和实验框架，捕获顺序学习和决策过程、潜在用户群体的行为和动态及其福利，来了解自动机器学习算法做出的公平决策的长期影响。这些知识将有助于在决策-行动循环的整个生命周期中设计正确的公平标准和干预机制，以确保长期公平的结果。这个项目的智力调查的中心是对循环中的人的关注，即一个人工智能-人类反馈循环，具有涉及人类参与的自动决策。我们关注公平算法决策的长期影响，同时明确建模并将人类代理纳入循环，提供了一个理论上严格的框架，以了解算法决策者在可预见的未来表现如何。该奖项反映了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。

Despite early successes and significant potential, algorithmic decision-making systems often inherit and encode biases that exist in the training data and/or the training process. It is thus important to understand the consequences of deploying and using machine learning models and provide algorithmic treatments to ensure that such techniques will ultimately serve the social good. While recent works have looked into the fairness issues in AI concerning the “short-term” measures, the long-term consequences and impacts of automated decision making remain unclear. The understanding of the long-term impact of a fair decision provides guidelines to policy-makers when deploying an algorithmic model in a dynamic environment and is critical to its trustworthiness and adoption. It will also drive the design of algorithms with an eye toward the welfare of both the makers and the users of these algorithms, with an ultimate goal of achieving more equitable outcomes. This project aims to understand the long-term impact of fair decisions made by automated machine learning algorithms via establishing an analytical, algorithmic, and experimental framework that captures the sequential learning and decision process, the actions and dynamics of the underlying user population, and its welfare. This knowledge will help design the right fairness criteria and intervention mechanisms throughout the life cycle of the decision-action loop to ensure long-term equitable outcomes. Central to this project’s intellectual inquiry is the focus on human in the loop, i.e., an AI-human feedback loop with automated decision-making that involves human participation. Our focus on the long-term impacts of fair algorithmic decision-making while explicitly modeling and incorporating human agents in the loop provides a theoretically rigorous framework to understand how an algorithmic decision-maker fares in the foreseeable future.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.