About
6
Publications
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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.
Current institution
Additional affiliations
October 2017 - May 2021
Position
- PhD Student
Description
- Supervised undergraduate students in the following courses: Philosophy and Scientific Practice, Epistemology and Metaphysics of Science.
Education
October 2017 - July 2021
February 2012 - February 2015
February 2011 - November 2011
Publications
Publications (6)
p-Hacking, the use of analytic techniques that may lead to distorted research results, is widely condemned on epistemic and practical grounds. The prevalent position on this questionable research practice is that p-hacking should be avoided because it raises the probability of obtaining false-positive results, which can have harmful practical conse...
Inferences from clinical research results to estimates of therapeutic effectiveness suffer due to various biases. I argue that predictions of medical effectiveness are prone to failure because current medical research overlooks the impacts of a particularly detrimental set of biases: meta-biases . Meta-biases are linked to higher-level characterist...
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...
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...
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...
