Uncanny Kinds

In the fields of healthcare and health research, there is particular interest using machine learning (ML) to generated novel or refined diagnostic, prognostic, risk, and treatment categories. This talk interrogates the nature of these categories and their implications for the people thus (re)categorised. It approaches these questions through the lens of the philosophical idea of ‘human kinds’. It asks to what extent health-related categories generated by ML might function as human kinds and, if so, whether they might  differ, in ethically significant ways, from socially-originating kinds. In doing so, it suggests that our understanding of responsible ML categorisation practices need to look beyond technical capabilities and clinical utility to consider wider personal and social impacts.

Bio

Emily Postan is a Chancellor’s Fellow in Bioethics at the University of Edinburgh Law School and a Deputy Director of the J Kenyon Mason Institute for Medicine Life Sciences and the Law. Her research principally focuses on ethical questions about the relationship between our bodies, our health, and our identities, and the ways that health technologies affect these relationships.  Her current research project ‘Identity by Algorithm’ explores the ethical implications of novel social categories generated by health applications of AI. Her wider research interests includes addressing the ethical challenges posed by data sharing, neurotechnologies, genomics, and assisted reproduction. Emily has a background in philosophy and as a policy-manager at the Scottish Government. She received her PhD from Edinburgh Law School in 2017. Her monograph ‘Embodied Narratives: Protecting Identity Interests through Ethical Governance of Bioinformation’ was published by CUP 2022.

X: @emily_postan

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