Clare Snyder

Clare Snyder is an Assistant Professor in the Department of Technology, Operations, and Statistics at the Stern School of Business, New York University. Her research examines human behavior within AI-enabled service systems, focusing on dynamic interactions between workers, customers, and algorithms in operational contexts.

Dr. Snyder earned her PhD from the Ross School of Business at the University of Michigan under advisors Samantha Keppler and Stephen Leider.

Her primary research interests include human-AI collaboration in service environments, behavioral responses to algorithmic decision aids, and generative AI integration in educational workflows. She investigates how system load and algorithm quality jointly influence worker reliance, how teachers adapt generative AI tools like ChatGPT for K-12 tasks, and why fairness outcomes degrade when humans mediate algorithmic decisions. Her methodological approach combines controlled laboratory experiments with longitudinal field studies in real-world settings.

Recent publications reveal critical nuances in human-AI systems: algorithmic efficiency gains emerge only under high workload and superior algorithm quality; generative AI adoption in education splits teachers into distinct usage clusters with varying productivity outcomes; and human deviations from fairness-designed algorithms paradoxically reduce overall equity. These findings challenge assumptions about AI implementation in service operations.

Dr. Snyder's scholarly contributions have been recognized through:

• 2024 Service Science Best Student Paper Finalist for "Algorithm Reliance, Fast and Slow"
• 2024 Thomas W. Leabo Fellowship for academic and teaching excellence

While her teaching portfolio includes Operations Management (TO 313) at Michigan Ross with high student evaluations, public sources contain no details about doctoral advising, grant funding, or laboratory affiliations.