Dr Sian Troath1
1ANU, Canberra, Australia
Biography:
Sian is currently a postdoctoral fellow at ANU, working on the politics of expertise involved in military AI, robotics, and autonomous systems, and the political economy of Australian militarism. Previously she was at the University of Canterbury, focused primarily on mapping the lethal autonomous weapons debate and the implications for alliances of new and emerging technologies. Her research also focuses on Australian foreign and defence policy, military imaginaries relating to emerging technologies, and theories of trust in international relations.
Abstract:
Modern warfare is an endeavour fixated on engineering conditions of certainty. In the attempt to divorce war from politics, robotics, autonomous systems, and artificial intelligence (RAS-AI) are merely the latest iteration of the desire to exert technological control over the fog of war. At the same time, machine learning and AI research is also fixated on the creation of certainty and predictability, given their reliance on a Cartesian and Newtonian philosophical foundation. The fixation on certainty emanating from both thinking about war and thinking about technology intensify when the two come together in the pursuit of military technological supremacy. In this article I am concerned with interrogating the intersection between the Newtonian and Cartesian intellectual inheritances of AI and machine learning, and ideas about precision, just and ethical war, and transparency and omniscience on the battlefield. I will do so in relation to the role of 'trust' and 'ethics' as central discourses and policy pursuits. As militaries turn to new and emerging technologies to maintain or achieve a technological edge over their perceived adversaries, they create new imaginaries of future war – alongside the technologists, academics, and defence scientists crafting new terms, ideologies, and frameworks for making sense of these technologies. Unpicking these imaginaries and interrogating their attempt to achieve the impossible is essential to understanding the current fixation on RAS-AI and the ideas emerging alongside these technologies.