Uploaded April 2023 | Updated September 2026, 2 days ago
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] 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.
davidwidder.me
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] 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.
davidwidder.me
