Date & Time:
November 11, 2024 10:00 am – 11:00 am
Location:
Crerar 346, 5730 S. Ellis Ave., Chicago, IL,
11/11/2024 10:00 AM 11/11/2024 11:00 AM America/Chicago Megha Srivastava (Stanford)- New Challenges of Trust with Large-Scale AI Systems Crerar 346, 5730 S. Ellis Ave., Chicago, IL,

Abstract: Today’s large-scale AI systems, trained with > 200 billion parameters over massive datasets, create new challenges of trust as users have increasingly less control over all aspects of model development.
I will first do a deep dive on the challenge of auditing model training service providers, who currently fine-tune models on behalf of resource-poor clients for a fee without any guarantee of correctness. I will show how prior solutions to this “verifiable training” problem are non-robust due to hardware non-determinism, which we address via a threshold-based rounding scheme over intermediate computations during model training. I will then show how users of modern code-generation models may introduce accidental security vulnerabilities due to misplaced confidence. Finally, I will conclude by discussing ongoing work on the current limitations of methods that seek to establish trust via content provenance (e.g. watermarking, C2PA).

Speakers

Megha Srivastava

PhD Student, Stanford University

Megha Srivastava is a Ph.D. student at Stanford University, co-advised by Dorsa Sadigh and Dan Boneh. She is interested in addressing issues of reliability in machine learning models within the broader context of human-AI interaction. In addition to being supported by the NSF GRFP and IBM Ph.D. Fellowships, her research has been recognized with an ICML Best Paper Runner-Up Award and she was selected as a Rising Star in Machine Learning in 2023.

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