“Challenges and Frontiers in Deploying Transparent Machine Learning”

Explainable machine learning offers the potential to provide stakeholders with insights into model behavior, yet there is little understanding of how organizations use these methods in practice. In this talk, Bhatt will discuss recent research exploring how organizations view and use explainability. He finds that most deployments are not for end-users but rather for machine learning engineers, who use explainability to debug the model. There is thus a gap between explainability in practice and the goal of external transparency since explanations are primarily serving internal stakeholders. Providing useful external explanations requires careful consideration of the needs of stakeholders, including end-users, regulators, and domain experts. Despite this need, little work has been done to facilitate inter-stakeholder conversation around explainable machine learning. To help address this gap, Bhatt reports findings from a closed-door, day-long workshop between academics, industry experts, legal scholars, and policymakers to develop a shared language around explainability and to understand the current shortcomings of and potential solutions for deploying explainable machine learning in the service of external transparency goals.

About the Speaker

Umang Bhatt is a Ph.D. candidate in the Machine Learning Group at the University of Cambridge. His expertise lies in human-machine collaboration and in trustworthy machine learning, spanning the fairness, robustness, and explainability of AI systems. He studies how to create AI systems that explain their predictions to stakeholders, leverage stakeholder expertise for better human-machine team performance, and interact with stakeholders to account for their goals and values. Currently, Umang is an Enrichment Student at The Alan Turing Institute and a Student Fellow at the Leverhulme Centre for the Future of Intelligence. Previously, he was a Fellow at the Mozilla Foundation and a Research Fellow at the Partnership on AI. He holds a B.S. and M.S. in Electrical and Computer Engineering from Carnegie Mellon University.

About the Young Achievers Symposium

The Young Achievers Symposium highlights early career researchers in diverse fields of AI for social impact. The symposium series seeks to focus on emerging research, stimulate discussions, and initiate collaborations that can advance research in artificial intelligence for societal benefit. All events in the series are free and open to the public unless otherwise noted. Penn State students, postdoctoral scholars, and faculty with an interest in socially responsible AI applications are encouraged to attend.