This article draws on key normative principles grounded in important values – solidarity, partiality and friendliness – in African philosophy to think critically and deeply about the ethical challenges around returning individual genetic research findings in African genomics research. Precisely, we propose that the normative implication of solidarity, partiality and friendliness is that returning findings should be considered as a gesture of goodwill to participants to the extent that it constitutes acting for their well-being. Concretely, the value of friendliness may (...) imply that one ought to return actionable results to participants even when their preferences regarding feedback are unknown. Notwithstanding, returning individual genetic results will have a cost implication. The cost of feeding back is relevant in the context of African genomics research projects, which are often funded by international sponsors and should be researched further. (shrink)

Developing and implementing artificial intelligence (AI) systems in an ethical manner faces several challenges specific to the kind of technology at hand, including ensuring that decision-making systems making use of machine learning are just, fair, and intelligible, and are aligned with our human values. Given that values vary across cultures, an additional ethical challenge is to ensure that these AI systems are not developed according to some unquestioned but questionable assumption of universal norms but are in fact compatible with the (...) societies in which they operate. This is particularly pertinent for AI research and implementation across Africa, a ground where AI systems are and will be used but also a place with a history of imposition of outside values. In this paper, we thus critically examine one proposal for ensuring that decision-making systems are just, fair, and intelligible—that we adopt a principle of explicability to generate specific recommendations—to assess whether the principle should be adopted in an African research context. We argue that a principle of explicability not only can contribute to responsible and thoughtful development of AI that is sensitive to African interests and values, but can also advance tackling some of the computational challenges in machine learning research. In this way, the motivation for ensuring that a machine learning-based system is just, fair, and intelligible is not only to meet ethical requirements, but also to make effective progress in the field itself. (shrink)

The silencing of the epistemologies, theories, principles, values, concepts and experiences of the global South constitutes a particularly egregious epistemic injustice in bioethics. Our shared responsibility to rectify that injustice should be at the top of the ethics agenda. That it is not, or only is in part, is deeply problematic and endangers the credibility of the entire field. As a first step towards reorienting the field, this paper offers a comprehensive account of epistemic justice for global health ethics. We (...) first introduce several different conceptions of justice and decolonisation in relation to knowledge, purposefully drawing on work emanating from the global South as well as the global North. We then apply those conceptions to the global health ethics context to generate a tripartite account of the layers of epistemic justice in the field: who is producing ethics knowledge; what theories and concepts are being applied to produce ethics knowledge; and whose voices are sought, recorded and used to generate ethics knowledge. These layers reflect that the field spans conceptual and empirical research. We conclude by proposing that, going forward, three avenues are key to achieve greater epistemic justice at each layer and to help decolonise global health ethics: namely, understanding the problem, dialogue and structural change. (shrink)