ByteCast Ep86: Cynthia Rudin

Why It Matters

Rudin’s work reframes how organizations build and deploy AI in high-stakes areas—healthcare, policing, infrastructure—by demonstrating that transparency can increase accuracy, trust, and accountability, reducing harm from opaque models. This has direct operational and policy implications for regulators and institutions that rely on algorithmic decision-making.

Summary

Cynthia Rudin, a Duke computer science professor and pioneer in interpretable machine learning, discusses her career challenging the notion that transparency must be sacrificed for accuracy in AI. She recounts applied successes where interpretable models outperformed black boxes—helping power engineers troubleshoot NYC manhole failures and enabling the Series Finder algorithm to identify crime series later adopted by the NYPD. Rudin emphasizes working with domain experts and messy real-world data, showing that interpretable models can both improve performance and foster collaboration. Her work has earned major recognition, including the 2022 Squirrel AI award for AI for the benefit of humanity.

Original Description

In this episode of ACM ByteCast, Rashmi Mohan hosts 2025 ACM Fellow Cynthia Rudin, the Gilbert, Louis, and Edward Lehrman Distinguished Professor of Computer Science, Electrical and Computer Engineering, Statistical Science, Mathematics, and Biostatistics and Bioinformatics at Duke University, where she leads the Interpretable Machine Learning Lab. Her lab, which seeks to design predictive ML models that people can understand, focuses on areas including healthcare, criminal justice, and energy reliability. Among her honors, she has received the Squirrel Award for Artificial Intelligence from the Association for the Advancement of Artificial Intelligence (AAAI), as well as the IJCAI John McCarthy Award. Rudin was recently named an ACM Fellow for contributions to and leadership in interpretable machine learning and societal applications.

In the interview, Cynthia clarifies the crucial distinction between "interpretable" and “explainable" AI and makes the argument that true interpretability is foundational to trustworthy, ethical AI. She shares her extensive field experience collaborating with Con Edison engineers on power grid maintenance, neurologists on medical diagnostics, and the Cambridge Police Department on crime series detection, countering the widespread industry myth that AI performance must be sacrificed for transparency. She describes an innovative paradigm her lab developed to solve the "interaction bottleneck" between data scientists and domain experts, leveraging "Rashomon sets" to generate millions of equally accurate models simultaneously, using human-computer interaction (HCI) tools to create visual, encyclopedia-like interfaces.