Annie Liang

Annie Liang is an Associate Professor in the Department of Economics at Northwestern University. Her research focuses on the intersection of economic theory, machine learning, and behavioral/experimental economics, with three main themes: the welfare implications of machine learning algorithms, using machine learning to improve economic modeling, and dynamic information acquisition. She holds a PhD from Harvard University and is affiliated with the university's Economics department. Her work can be explored on her personal website.

Annie Liang is a researcher at Northwestern University with expertise in Economics and Computer Science. Her work explores the intersection of these fields, focusing on economic models, algorithmic fairness, and the application of machine learning in economic theory. She investigates how economic models can be evaluated and improved using data-driven methods, while also addressing issues of fairness and accuracy in algorithm design. Her research includes topics such as information aggregation, learning traps, and the role of context in human and algorithmic decision-making. Liang's publications highlight the importance of understanding the limitations of economic theories and the potential of AI to enhance predictive accuracy and fairness.