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The Governance of Artificial Intelligence (AI) Program at the University of Oxford's Future of Humanity Institute focuses on the political challenges associated with the rapid development of artificial intelligence. The [...]

Major theories of military innovation focus on relatively narrow technological developments, such as nuclear weapons or aircraft carriers. Arguably the most profound military implications of technological change, however, come from more fundamental advances arising from 'general-purpose technologies' (GPTs), such as the steam engine, electricity, and the computer. Building from scholarship on GPTs and economic growth, we argue that the effects of GPTs on military effectiveness are broad, delayed, and shaped by indirect productivity spillovers. We label this impact pathway a 'general-purpose military transformation' (GMT). Contrary to studies that predict GPTs will rapidly diffuse to militaries around the world and narrow gaps in capabilities, we show that GMTs can reinforce existing balances if leading militaries have stronger linkages to a robust industrial base in the GPT than challengers. Evidence from electricity's impact on military affairs, covering the late nineteenth and early twentieth centuries, supports our propositions about GMTs. To probe the explanatory value of our theory and account for alternative interpretations, we compare findings from the electricity case to the military impacts of submarine technology, a non-GPT that emerged in the same period. Finally, we apply our findings to contemporary debates about artificial intelligence, which could plausibly cause a profound GMT. [ABSTRACT FROM AUTHOR]

Machine learning (ML) and artificial intelligence (AI) researchers play an important role in the ethics and governance of AI, including through their work, advocacy, and choice of employment. Nevertheless, this in uential group's attitudes are not well understood, undermining our ability to discern consensuses or disagreements between AI/ML researchers. To examine these researchers' views, we conducted a survey of those who published in two top AI/ML conferences (N = 524). We compare these results with those from a 2016 survey of AI/ML researchers (Grace et al., 2018) and a 2018 survey of the US public (Zhang & Dafoe, 2020). We find that AI/ML researchers place high levels of trust in international organizations and scientific organizations to shape the development and use of AI in the public interest; moderate trust in most Western tech companies; and low trust in national militaries, Chinese tech companies, and Facebook. While the respondents were overwhelmingly opposed to AI/ML researchers working on lethal autonomous weapons, they are less opposed to researchers working on other military applications of AI, particularly logistics algorithms. A strong majority of respondents think that AI safety research should be prioritized and that ML institutions should conduct pre-publication review to assess potential harms. Being closer to the technology itself, AI/ML researchers are well placed to highlight new risks and develop technical solutions, so this novel attempt to measure their attitudes has broad relevance. The findings should help to improve how researchers, private sector executives, and policymakers think about regulations, governance frameworks, guiding principles, and national and international governance strategies for AI. [ABSTRACT FROM AUTHOR]

What resources and technologies are strategic? Policy and theoretical debates often focus on this question, since the "strategic" designation yields valuable resources and elevated attention. The ambiguity of the very concept, however, frustrates these conversations. We offer a theory of when decision makers should designate assets as strategic based on the presence of important rivalrous externalities for which firms or military organizations will not produce socially optimal behavior on their own. We distill three forms of these externalities, which involve cumulative-, infrastructure-, and dependency-strategic logics. Although our framework cannot resolve debates about strategic assets, it provides a theoretically grounded conceptual vocabulary to make these debates more productive. To illustrate the analytic value of our framework for thinking about strategic technologies, we examine the US-Japan technology rivalry in the late 1980s and current policy discussions about artificial intelligence. [ABSTRACT FROM AUTHOR]

We ask how the offense-defense balance scales, meaning how it changes as investments into a conflict increase. To do so we offer a general formalization of the offense-defense balance in terms of contest success functions. Simple models of ground invasions and cyberattacks that exploit software vulnerabilities suggest that, in both cases, growth in investments will favor offense when investment levels are sufficiently low and favor defense when they are sufficiently high. We refer to this phenomenon as offensive-then-defensive scaling or OD-scaling. Such scaling effects may help us understand the security implications of applications of artificial intelligence that in essence scale up existing capabilities. [ABSTRACT FROM AUTHOR]