As all good policy practitioners know, all regulatory activities are constrained by the agency problem: those who are being regulated know far more of what is going on than those who are charged with overseeing their activities.
But there are even more problems of information asymmetry associated with AI regulation, because even those who are on the “inside” are still learning about the capabilities of the new technologies they are developing.
AI technologies , because of their human and constantly evolving characteristics, along dimensions of which their human developers are not entirely sure about, have the potential to create totally unexpected “surprises” – that is, the true “black swans” that not even prudent risk management can reasonably expected to anticipate. This invokes Frank Knight’s insightful 1921 distinction between risk – which can be measured and quantified – and uncertainty, which cannot.
Making decisions under such Knightian uncertainty is both an art and a science, relying on intuitive judgement and tacit knowledge built through experience in navigating complex, unstable environments synthesized with what little tangible…
