Epistemic marginalization in the LAWS discourse as a form of epistemic misalignment with the global South

The assumptions and value commitments in the discourse on and development of Lethal Autonomous Weapons Systems (LAWS) do not reflect the plurality of perspectives from the South. Both the regulatory discourse on LAWS and the development of these military Artificial Intelligence (AI) systems are entangled with epistemic forms of exclusion. LAWS suffer from, on the one hand, a vulnerability to unintended risks and failures due to an epistemic misrepresentation of targets and cultural particulars, and, on the other hand, the failure to distinguish between civilian and military targets rooted in algorithmic bias and exclusion of affected parties from the training data and algorithm development. In this paper, it is argued that three forms of exclusion of the global South stand out: a lack of training data for the algorithmic systems, stakeholder exclusion from the development of LAWS, and the exclusion from the regulation discourse. These exclusions are conceptualized as a form of discriminatory epistemic injustice. As an extension of a taxonomy of algorithmic epistemic injustice, we make the case for the concept of algorithmic epistemic marginalization as explainer for the epistemic forms of exclusion—‘epistemic misalignments’ of LAWS with the global South. Algorithmic epistemic marginalization is defined as ‘the rendering marginal of the experiences of citizens from the global South and their epistemic-cultural particulars in LAWS development and regulation’ and is shown to lead to representational harm and system failure. Finally, the importance of institutional safeguards and epistemic inclusivity for minimizing the epistemic misalignments at the base of LAWS’ algorithmic epistemic marginalization.