7 citations · 11 across the 3 of their papers we have counts for
3 papers
Quantifying detection rates for dangerous capabilities: a theoretical model of dangerous capability evaluations
Paolo Bova, Alessandro Di Stefano, The Anh Han
We present a quantitative model for tracking dangerous AI capabilities over time. Our goal is to help the policy and research community visualise how dangerous capability testing c…
Trust AI Regulation? Discerning users are vital to build trust and effective AI regulation
Zainab Alalawi, Paolo Bova, Theodor Cimpeanu +8
There is general agreement that some form of regulation is necessary both for AI creators to be incentivised to develop trustworthy systems, and for users to actually trust those s…
Both eyes open: Vigilant Incentives help Regulatory Markets improve AI Safety
Paolo Bova, Alessandro Di Stefano, The Anh Han
In the context of rapid discoveries by leaders in AI, governments must consider how to design regulation that matches the increasing pace of new AI capabilities. Regulatory Markets…