Uncanny Human Kinds
The Problematic Social Life of Deep Learning-Generated Health Categories
Bibliographic Data
| ID | 22024388 |
|---|---|
| Authors | Emily Postan (0000-0002-6705-0921, Edinburgh College, corresponding author) |
| Year | 2026 |
| Volume | 39 |
| Issue | 3 |
| Publication date | 2026-09-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Philosophy & Technology (JOURNAL) |
| Journal identifiers | ISSN: 2210-5433 • E-ISSN: 2210-5441 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s13347-026-01125-1 |
| OpenAlex | W7166159280 |
| Language | EN |
| References cited | 77 |
There is marked enthusiasm about the potential for deep learning (DL) systems to generate new data-driven diagnostic, predictive, and prognostic health categories that could support more effective, precisely targeted healthcare. This article argues that, in order to determine whether these novel categories would be ethically desirable ones, we cannot look only to their clinical utility, or to the threats posed to this by widely recognised problems of algorithmic error, bias, and opacity. This is because health categories do not only classify disease and inform care. They also classify people. And they do so for ethically impactful purposes that extend far beyond the clinic. In light of this, and to address the central question of whether novel DL-generated health categories would constitute good or supportive ways of classifying people, this article appeals to the concept of human kinds. This framing serves to highlight how diagnostic and predictive health categories can shape the identities and relationships of the people classified in ethically significant ways. It is argued here that DL-generated health categories could occupy the roles of human kinds. However, they are likely to exhibit particular qualities that distinguish them from familiar human kinds that have originated in human practices. And these qualities could result in marked disjunctions between, on one hand, the meanings and membership of these kinds, and on the other hand, members’ characteristics and lived contexts, resulting in uncannily abstracted kinds. As a result of this uncanniness, kinds originating in DL-generated health categories could significantly undermine the wellbeing and interests of their members in a range of important ways. For this reason, when assessing the ethical desirability of using DL to generate data-driven diagnoses and health predictions, it is necessary to assess not only the clinical value of these diagnoses and predictions, but also the risks of seeding uncanny human kinds
Enthusiasm · Human health · Human life · Philosophy of technology · Social life · Uncanny · Artificial Intelligence in Healthcare and Education · Explainable Artificial Intelligence (XAI · Machine Learning in Healthcare
We Are Data
How We Became Posthuman
Machine Learners
Diagnostic and Statistical Manual of Mental Disorders
Epistemic Injustice and the Preservation of Ignorance
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Machine learning in medicine
The looping effects of human kinds
Dissecting racial bias in an algorithm used to manage the health of populations
The Epistemology and Morality of Human Kinds
The Sources of Normativity
Digital personomics
The new self-advocacy activism in psychiatry
Defining the undefinable
Should medicine be colour blind
Practical, epistemic and normative implications of algorithmic bias in healthcare artificial intelligence
Artificial intelligence and identity
From “Personalized” to “Precision” Medicine
Artificial Intelligence and Black‐Box Medical Decisions
The Construction of Human Kinds
Categories We Live By
Epistemic Injustice and Illness
Artificial Intelligence and Patient-Centered Decision-Making
Breast Cancer Genes and the Gendering of Knowledge
Genetic Testing for Hereditary Breast and Ovarian Cancer
The ethics of AI in health care
The production of prediction
Accommodating risk
II. The Gift of Life
Genetics, biosocial groups & the future of identity
Resisting Reality
The ethics of algorithms
The Genomic Revolution and Beliefs about Essential Racial Differences
A New Algorithmic Identity
Geneticization of Deviant Behavior and Consequences for Stigma
Neurobiological narratives
| Highly cited | No |
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