This paper aims to provide a roadmap to AI governance. In contrast to the reigning paradigms, we argue that AI governance should not be merely a reactive, punitive, status-quo-defending enterprise, but rather the expression of an expansive, proactive vision for technology—to advance human flourishing. Advancing human flourishing in turn requires democratic/political stability and economic empowerment. Our overarching point is that answering questions of how we should govern this emerging technology is a chance not merely to categorize and manage narrow risk (...) but also to construe the risks and opportunities much more broadly, and to make correspondingly large investments in public goods, personnel, and democracy itself. To lay out this vision, we take four steps. First, we define some central concepts in the field, disambiguating between forms of technological harms and risks. Second, we review normative frameworks governing emerging technology that are currently in use around the globe. Third, we outline an alternative normative framework based in power-sharing liberalism. Fourth, we walk through a series of governance tasks that ought to be accomplished by any policy framework guided by our model of power-sharing liberalism. We follow these with proposals for implementation vehicles. (shrink)

According to the mental model theory, causes and enablers differ in meaning, and therefore in their logical consequences (Goldvarg & Johnson-Laird, 2001). They are consistent with different possibilities. Recent psychological studies have argued to the contrary, and suggested that linguistic cues guide this distinction (Kuhnmünch & Beller, 2005). The issue is important because neither British nor American law recognizes this distinction (e.g., Roberts & Zuckerman, 2004). Yet, in our view, it is central to human conceptions of causality. Hence, in (...) two experiments, we examined our participants’ ability to distinguish between causes and enablers in scenarios describing the actions of two agents and a subsequent outcome, e.g.: ‘Mary threw a lighted cigarette into a bush. Just as the cigarette was going out, Laura deliberately threw petrol on it. The resulting fire burnt down her neighbor’s house.’ Here Mary enabled the fire to occur, whereas Laura caused the fire to occur. (shrink)