I am a philosopher of medicine and Artificial Intelligence (AI), focusing on questions at the intersection of evidence and ethics. I am currently Associate Professor of Interdisciplinary Education and Co-Director of LSE100 at the London School of Economics. I have also been Lecturer in Philosophy of AI and Data Ethics at the New College of the Humanities. I am an Affiliate of the LSE Data Science Institute.
My research focuses on Evidence-Based Medicine, hierarchies of evidence, and other problems related to evidence and ethics in biomedical research, medical genomics, machine learning, and complementary & alternative medicine. This work was profiled in Science News.
I received my PhD from LSE in 2016, with a thesis focused on ‘Hierarchies of Evidence’, available here. Hierarchies of Evidence are a popular tool for appraising evidence used in medical practice, and a common way of teaching evidence appraisal to medical students.
My work is now structured as a series of papers which form the basis for an upcoming book on evidence appraisal in medicine, along with a series of chapters of the series Philosophy of Diagnosis, and standalone articles on themes such as scientific realism and the history of medicine, evidence in education research, scientific demarcation, and philosophical questions in AI particularly relating to language models.
The articles available online are:
- (2022) The Pyramid Schema: The Origins and Impact of Evidence Pyramids
- (2022) The Machine Scientists: Iatrophysics and Selective Scientific Realism
- (2021) Imitating Imitation: a response to Floridi & Chiriatti
- (2021) Minding the Gaps: Statistical Misrepresentation in Attainment Gap Research
- (2020) Causal Relevance (Philosophy of Diagnosis, Part 3)
- (2020) Pathognomy, Sine Qua Non and Constitutive Matching (Philosophy of Diagnosis, Part 2)
- (2020) Problems of Diagnosis (Philosophy of Diagnosis, Part 1)
- (2019) The Dismal Disease: Temozolomide and the Interaction of Evidence
- (2019) The Authority of Evidence-Based Medicine
- (2019) The Positivity Machine: “Evidence-Based Alternative Medicine” and Grades of Recommendation
- (2018) The Parachute Problem: Extracorporeal Life Support and the Demand for Trials
- (2018) The Avoidable Scandal: Benoxaprofen and Theories of Medical Evidence
- (2017) Pluralism and the Problems of Demarcation
- (2015) Hierarchies of Evidence in Evidence-Based Medicine
- (2013) The Disunity of Evidence-Based Medicine
As Co-Director of LSE100, I work on the delivery of LSE100, LSE’s flagship interdisciplinary course which is studied by all undergraduates in their first year. LSE100 delivers interdisciplinary seminars accompanied by bespoke online content featuring LSE academics. It serves as a forum for students and academics of all disciplines to meet and discuss major societal challenges, and a laboratory for pedagogic innovation in higher education. LSE students select one of three big social, political, economic and philosophical challenges to study: “How can we control AI?“, “How can we transform our climate futures?” and “How can we create a fair society?“.
Recently, my interests in AI and medicine have been overlapping in a pair of new projects: (1) on the philosophy of diagnostics, which brings differences and commonalities between AI and clinician diagnostics into focus, and (2) on the disparities between information-theoretic and socio-political conceptualisations of anonymity and privacy with respect to the data access needed to train new machine learning models for diagnostics and therapeutics.
I have a PGCE in higher education and am a Fellow of the Higher Education Academy. Alongside LSE100, I have taught a range of courses in LSE’s Philosophy department at undergraduate and masters level, and examined MA student dissertations in Philosophy of Artificial Intelligence.
This site is an ongoing repository for all of my work. I have criticised academic publishing models extensively in the past, and so offer all of my research freely here.
I am always interested to hear about challenges faced in the analysis of evidence by both academics and practitioners working in healthcare and in artificial intelligence research. I am part of ongoing projects with colleagues working in fields including rheumatology, orthopaedic surgery and physiotherapy, and machine learning. My philosophical work is driven by the engagement and challenges faced by medical practitioners. If you have an interest in evidence hierarchies or philosophical challenges in medical evidence, do get in touch.
Contact: c.j.blunt <at> lse.ac.uk