Structural Injustice has become a hugely important concept in the field of political theory with the work of Iris Marion Young central to debates on what it is, what motivates it and how it should be addressed. In this article, I focus on a particular thread in Young’s account of structural injustice which I argue is all too often overlooked - the untraceability of structural injustice. This is not only a constant theme in Young’s account of structural injustice, it is, (...) in fact, a defining feature. By ‘untraceable’, Young means that structural injustice is distinct from other sorts of injustices for which there is a traceable agent at fault – these sorts of faut-based injustices fall under what Young calls the ‘liability model of injustice’. For the purposes of exploring the concept of structural injustice, I follow the logic of the claim that structural injustice is untraceable to see where it takes us in conversation with Young’s critics. I conclude that Young’s account is not only defensible with some clarification and development but vital politically. That said, I also find Young’s account of political responsibility for structural injustice wanting and accordingly I propose a way of understanding what I see as a dynamic transition from structural to fault-based injustices which I argue Young’s more ‘static’ account fails to accommodate. This is important, I suggest, for thinking about the ways in which responsibility for different forms of injustice operate and change. _Contemporary Political Theory_ (2023). https://doi.org/10.1057/s41296-023-00634-4. (shrink)

Artificial intelligence-based (AI) technologies such as machine learning (ML) systems are playing an increasingly relevant role in medicine and healthcare, bringing about novel ethical and epistemological issues that need to be timely addressed. Even though ethical questions connected to epistemic concerns have been at the center of the debate, it is going unnoticed how epistemic forms of injustice can be ML-induced, specifically in healthcare. I analyze the shortcomings of an ML system currently deployed in the USA to predict patients’ likelihood (...) of opioid addiction and misuse (PDMP algorithmic platforms). Drawing on this analysis, I aim to show that the wrong inflicted on epistemic agents involved in and affected by these systems’ decision-making processes can be captured through the lenses of Miranda Fricker’s account of hermeneutical injustice. I further argue that ML-induced hermeneutical injustice is particularly harmful due to what I define as an automated hermeneutical appropriation from the side of the ML system. The latter occurs if the ML system establishes meanings and shared hermeneutical resources without allowing for human oversight, impairing understanding and communication practices among stakeholders involved in medical decision-making. Furthermore and very much crucially, an automated hermeneutical appropriation can be recognized if physicians are strongly limited in their possibilities to safeguard patients from ML-induced hermeneutical injustice. Overall, my paper should expand the analysis of ethical issues raised by ML systems that are to be considered epistemic in nature, thus contributing to bridging the gap between these two dimensions in the ongoing debate. (shrink)