Uploaded April 2025 | Updated September 2026, 1 day ago
Evan Shelhamer is an assistant professor at UBC in Vancouver and member of the Vector Institute. His research is on visual recognition, self-supervised learning without annotations, and robustness by adaptation. He earned his PhD at UC Berkeley advised by Prof. Trevor Darrell. He was the lead developer of the Caffe open-source deep learning framework from version 0.1 to 1.0. His research and service have received awards including the best paper honorable mention at CVPR'15 for fully convolutional networks and the Mark Everingham award at ICCV'17, the open-source award at MM'14, and the test-of-time award at MM'24 for Caffe. He likes to brew coffee and community, and his latest organizing efforts include the 1st workshop on test-time adaptation at CVPR'24 and the 3rd workshop on machine learning for remote sensing at ICLR'25. He is new the Pacific NW and excited to experience every kind of rain and explore substitutes for sunshine.
Evan Shelhamer is an assistant professor at UBC in Vancouver and member of the Vector Institute. His research is on visual recognition, self-supervised learning without annotations, and robustness by adaptation. He earned his PhD at UC Berkeley advised by Prof. Trevor Darrell. He was the lead developer of the Caffe open-source deep learning framework from version 0.1 to 1.0. His research and service have received awards including the best paper honorable mention at CVPR'15 for fully convolutional networks and the Mark Everingham award at ICCV'17, the open-source award at MM'14, and the test-of-time award at MM'24 for Caffe. He likes to brew coffee and community, and his latest organizing efforts include the 1st workshop on test-time adaptation at CVPR'24 and the 3rd workshop on machine learning for remote sensing at ICLR'25. He is new the Pacific NW and excited to experience every kind of rain and explore substitutes for sunshine.







![Open AI: considering the ethical upsides and downsides of Open AI development
Abstract:
In this talk, I will discuss the ethical upsides and downsides of releasing AI openly.
I will first present our FAccT’22 paper [1], where we interview contributors to an open source Deepfake tool about their sense of responsibility and agency to prevent harm. We show that open source licenses and norms combine with notions of technological inevitability and neutrality to lead contributors to disavow responsibility for harmful ways their tool is used.
I will then broaden to discuss other work examining AI openness, situated in the context of “Open”AI’s U-turn on openness. I will discuss benefits of AI openness, such as supporting open science, and enabling wider scrutiny for harms such as bias, and downsides, such as enabling the proliferation of powerful tools which can be used to harm.
I will then conclude by enumerating and advocating for a variety of “middle ground” approaches to AI openness, including methods of norm setting, ethical licenses, release gating, or hard technical restrictions, before opening up discussion for other ways of tackling this thorny problem.
[1] https://dl.acm.org/doi/abs/10.1145/3531146.3533779
Bio:
David Gray Widder (he/him) studies how people creating “Artificial Intelligence” systems think about the downstream harms their systems make possible. He is a Doctoral Student in the School of Computer Science at Carnegie Mellon University, and previously worked at Intel Labs, Microsoft Research, and NASA’s Jet Propulsion Laboratory. He was born in Tillamook, Oregon, and raised in Berlin and Singapore. He maintains a conceptual-realist artistic practice, advocates against police terror and pervasive surveillance, and enjoys distance running.
https://davidwidder.me/ Open AI: considering the ethical upsides and downsides of Open AI development](https://i.ytimg.com/vi/HZP3kps9TsU/mqdefault.jpg)


