CAREER: Towards Long-term Fairness in Sequential Decision Making

University Of Arkansas, Fayetteville AR

Investigators

Abstract

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.