Adrian Erasmus

Adrian Erasmus
University of Alabama | UA · Department of Philosophy

Doctor of Philosophy

About

4
Publications
586
Reads
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28
Citations
Citations since 2017
4 Research Items
28 Citations
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Introduction
I am a philosopher of science specializing in Philosophy of Medicine. I am interested in conceptual and methodological questions about medical inference, and the intersection of values and epistemic concerns in medical artificial intelligence. I am an Assistant Professor in the Department of Philosophy at the University of Alabama and associate member at the McCollough Institute for Pre-medical Scholars.
Additional affiliations
October 2017 - June 2019
University of Cambridge
Position
  • PhD Student
Description
  • Supervised undergraduate students in the following courses: Metaphysics, Epistemology, and Logic.
October 2017 - May 2021
University of Cambridge
Position
  • PhD Student
Description
  • Supervised undergraduate students in the following courses: Philosophy and Scientific Practice, Epistemology and Metaphysics of Science.
February 2014 - June 2017
University of Pretoria
Position
  • Lecturer
Description
  • Courses developed and taught: Philosophy of Science (for medical students); Philosophy of Medicine (for medical students). Courses taught:​ Moral and Political Philosophy.
Education
October 2017 - July 2021
University of Cambridge
Field of study
  • History and Philosophy of Science
February 2012 - February 2015
University of Johannesburg
Field of study
  • Philosophy
February 2011 - November 2011
University of Johannesburg
Field of study
  • Philosophy

Publications

Publications (4)
Article
Full-text available
In a recent reply to our article, “What is Interpretability?,” Prasetya argues against our position that artificial neural networks are explainable. It is claimed that our indefeasibility thesis—that adding complexity to an explanation of a phenomenon does not make the phenomenon any less explainable—is false. More precisely, Prasetya argues that u...
Article
Full-text available
We argue that artificial networks are explainable and offer a novel theory of interpretability. Two sets of conceptual questions are prominent in theoretical engagements with artificial neural networks, especially in the context of medical artificial intelligence: (1) Are networks explainable, and if so, what does it mean to explain the output of a...
Thesis
This dissertation explores several conceptual and methodological features of medical science that influence our ability to accurately predict medical effectiveness. Making reliable predictions about the effectiveness of medical treatments is crucial to mitigating death and disease and improving individual and population health, yet generating such...

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