Machine learning-based AI algorithms lack transparency. In this article, I offer an interpretation of AI’s explainability problem and highlight its ethical saliency. I try to make the case for the legal enforcement of a strong explainability requirement: human organizations which decide to automate decision-making should be legally obliged to demonstrate the capacity to explain and justify the algorithmic decisions that have an impact on the wellbeing, rights, and opportunities of those affected by the decisions. This legal duty can be derived (...) from the demands of Rawlsian public reason. In the second part of the paper, I try to show that the argument from the limitations of human cognition fails to get AI off the hook of public reason. Against a growing trend in AI ethics, my main argument is that the analogy between human minds and artificial neural networks fails because it suffers from an atomistic bias which makes it blind to the social and institutional dimension of human reasoning processes. I suggest that developing interpretive AI algorithms is not the only possible answer to the explainability problem; social and institutional answers are also available and in many cases more trustworthy than techno-scientific ones. (shrink)

This article draws on an empirical investigation of how Australia’s cervical screening program came to be the way it is. The study was carried out using grounded theory methodology and primarily uses interviews with experts involved in establishing, updating or administering the program. We found strong differences in experts’ normative evaluations of the program and beliefs about optimal ways of achieving the same basic outcome: a reduction in morbidity and mortality caused by invasive cervical cancer. Our analysis demonstrates how variations (...) in values and preferences associated with key concepts underpinning public health such as benefit, harm and burden led to different perspectives on the purpose of an organized cervical screening program. These variations were largely driven by different bases of professional experience. Differently conceived purposes in turn led to conflict over how the program should be operationalized. Following Frith’s model of empirical bioethics, the results of the study both draw on and inform the existing public health and screening ethics literature. (shrink)