The present debate over the creation and potential deployment of lethal autonomous weapons, or ‘killer robots’, is garnering more and more attention. Much of the argument revolves around whether such machines would be able to uphold the principle of noncombatant immunity. However, much of the present debate fails to take into consideration the practical realties of contemporary armed conflict, particularly generating military objectives and the adherence to a targeting process. This paper argues that we must look to the targeting process (...) if we are to gain a fuller picture of the consequences of creating or fielding lethal autonomous robots. This paper argues that once we look to how militaries actually create military objectives, and thus identify potential targets, we face an additional problem: the Strategic Robot Problem. The ability to create targeting lists using military doctrine and targeting processes is inherently strategic, and handing this capability over to a machine undermines existing comman.. (shrink)

ABSTRACTAutonomous weapons systems pose many challenges in complex battlefield environments. Previous discussions of them have largely focused on technological or policy issues. In contrast, we focus here on the challenge of trust in an AWS. One type of human trust depends only on judgments about the predictability or reliability of the trustee, and so are suitable for all manner of artifacts. However, AWSs that are worthy of the descriptor “autonomous” will not exhibit the required strong predictability in the complex, changing (...) contexts of war. Instead, warfighters need to develop deeper, interpersonal trust that is grounded in understanding the values, beliefs, and dispositions of the AWS. Current acquisition, training, and deployment processes preclude the development of such trust, and so there are currently no routes for a warfighter to develop trust in an AWS. We thus survey three possible changes to current practices in order to facilitate the type of deep trust that is required for appropri... (shrink)