January 10, 2021
Skeptics in the Pub: Too Dangerous to Publish?
Navigating the High-Stakes Nature of Ai Research
This Thursday I am giving a talk at Skeptics in the Pub Online! Skeptics in the Pub is high on the list of things I miss about the UK, so I’m very glad that I can still participate virtually.
I’ll be speaking about responsible publication norms for AI research and how to navigate the tension between openness and caution, among other things. The event is open to all, and will be my first time on Twitch! Join us at:
- 11am PT
- 2pm ET
- 7pm GMT
The talk will be around 45mins, followed by a short break, and then a Q&A. I’ve also been informed that there will be a lively after-party on Zoom that has been known to go on until the early hours of the morn…
Watch Live at https://twitch.tv/sitp
The blurb and bio:
As AI becomes increasingly advanced, it promises many benefits but also comes with risks. How can we mitigate these risks while preserving scientific inquiry and openness? Who is responsible for anticipating the impacts of AI research, and how can they do so effectively? What changes, if any, need to be made to the peer review process? In this talk, we’ll explore these tensions and how they are playing out right now in the AI community. AI is not the first high-stakes, ‘dual-use’ field to face these questions. Taking inspiration from fields like cybersecurity and biosecurity, we’ll look at possible approaches to responsible publication, their strengths and limitations, and how they might be used in practice for AI.
Rosie Campbell leads the Safety-Critical AI program at the Partnership on AI, a multistakeholder nonprofit shaping the future of responsible AI. Her main focus is on responsible publication and deployment practices for increasingly advanced AI. Previously, she was Assistant Director of the Center for Human-Compatible AI at UC Berkeley, a Research Engineer at BBC R&D, and cofounder of Manchester Futurists. Her academic background spans physics, philosophy, and computer science. Rosie is also a productivity nerd and enjoys thinking about how to optimize systems, and how to use reason and evidence to improve the world.