“Enabling Fairness in Healthcare Through Machine Learning” Dr Geoff Keeling

Summer Colloquium Series on Stereotyping & Medical AI exploring philosophical issues around stereotyping in medicine and medical AI.




We are very pleased to announce our seventh and penultimate colloquium in this series of colloquia on Stereotyping & Medical AI, organised by the Sowerby Philosophy & Medicine Project:

Dr. Geoff Keeling (HAI, LCFI, Google) – “Enabling Fairness in Healthcare Through Machine Learning”

Dr. Geoff Keeling is an Interdisciplinary Ethics Fellow based between the Center for Ethics in Society and the Institute for Human-Centered Artificial Intelligence (HAI) at Stanford University. His work concerns the ethics of robotics and data-driven technologies, completing a PhD on the ethics of automated vehicle decision making. He is also an Associate Fellow at the Leverhulme Centre for the Future of Intelligence (LCFI), where he worked with Dr. Rune Nyrup on the Understanding Medical Black Boxes Project, looking at how tools from the philosophy of science might inform disputes about explainable machine learning in medicine.

About the Summer Colloquium Series on Stereotyping & Medical AI

The aim of this series on Stereotyping and Medical AI is to explore philosophical and in particular ethical and epistemological issues around stereotyping in medicine, with a specific focus on the use of artificial intelligence in health contexts.

We are particularly interested in whether medical AI that uses statistical data to generate predictions about individual patients can be said to “stereotype” patients, and whether we should draw the same ethical and epistemic conclusions about stereotyping by artificial agents as we do about stereotyping by human agents, i.e., medical professionals.

Other questions we are interested in exploring as part of this series include but are not limited to the following:

How should we understand “stereotyping” in medical contexts?

What is the relationship between stereotyping and bias, including algorithmic bias (and how should we understand “bias” in different contexts?)?

Why does stereotyping in medicine often seem less morally or epistemically problematic than stereotyping in other domains, such as in legal, criminal, financial, educational, etc., domains? Might beliefs about biological racial realism in the medical context explain this asymmetry?

When and why might it be wrong for medical professionals to stereotype their patients? And when and why might it be wrong for medical AI, i.e. artificial agents, to stereotype patients?

How do (medical) AI beliefs relate to the beliefs of human agents, particularly with respect to agents’ moral responsibility for their beliefs?

Can non-evidential or non-truth-related considerations be relevant with respect to what beliefs medical professionals or medical AI ought to hold? Is there moral or pragmatic encroachment on AI beliefs or on the beliefs of medical professionals?

What are potential consequences of either patients or doctors being stereotyped by doctors or by medical AI in medicine? Can, for example, patients be doxastically wronged by doctors or AI in virtue of being stereotyped by them?

We will be tackling these topics through a series of online colloquia hosted by the Sowerby Philosophy and Medicine Project at King’s College London. The colloquium series will feature a variety contributors from across the disciplinary spectrum. We hope to ensure a discursive format with time set aside for discussion and Q&A by the audience. This event is open to the public and all are welcome.

The event is finished.


Sep 02 2021


12:00 pm - 1:30 pm

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