The introduction of Autonomous Military Systems (AMS) onto contemporary battlefields raises concerns that they will bring with them the possibility of a techno-responsibility gap, leaving insecurity about how to attribute responsibility in scenarios involving these systems. In this work I approach this problem in the domain of applied ethics with foundational conceptual work on autonomy and responsibility. I argue that concerns over the use of AMS can be assuaged by recognising the richly interrelated context in which these systems will most (...) likely be deployed. This will allow us to move beyond the solely individualist understandings of responsibility at work in most treatments of these cases, toward one that includes collective responsibility. This allows us to attribute collective responsibility to the collectives of which the AMS form a part, and to account for the distribution of burdens that follows from this attribution. I argue that this expansion of our responsibility practices will close at least some otherwise intractable techno-responsibility gaps. (shrink)

Thesis (MA)--Stellenbosch University, 2014.

Many recent AI policies have been structured under labels that follow a particular trend: national or international guidelines, policies or regulations, such as the EU’s and USA’s ‘Trustworthy AI’ and China’s and India’s adoption of ‘Responsible AI’, use a label that follows the recipe of [agentially loaded notion + ‘AI’]. A result of this branding, even if implicit, is to encourage the application by laypeople of these agentially loaded notions to the AI technologies themselves. Yet, these notions are appropriate only (...) when applied to agents, which current AI technologies are not; and the concern is that this misapplication creates an incentive to inappropriately attribute trustworthiness or responsibility to AI technologies. We endeavour to show that we have good reason to avoid any general AI policy that uses agentially loaded labelling. We suggest labelling these policies not in terms of some qualification of AI, but rather in terms of our approach to the technology and its wider development and use context – focusing on being trustworthy and responsible about AI, rather than on trustworthy or responsible AI. (shrink)