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Normative Philosophy Of Science Lab
Philosophy of science is deeply normative. What makes a scientific explanation a good explanation? When are we justified in relying on a scientific model or interpretation? What role do values play in gaining scientific knowledge or understanding?
​The Normative Philosophy of Science Lab is based at Utrecht University and TU/e. We are passionate about investigating the function that values and norms have in science, how epistemic normative judgments influence model acceptance and use, and how philosophy of science methodology might even help answer questions in ethics and epistemology. Our lab has a focus on machine learning technologies used in science and society.
Our lab is unique in striving to develop philosophical theories that have impact in computer science and society, but also make progress on fundamental issues in philosophy of science regarding explanation, idealization, scientific understanding, and more.
Adam Mehdi Arafan
(PhD candidate, TU/e, Computer Science)
PhD expected 2027
Adam is passionate about how counterfactual probing of machine learning models can help diagnose whether a model is fair, and how we might be able to define and operationalize what a fair counterfactual is. Adam is also a member of Capgemni’s AI research lab.
Lab members:
Yeji Streppel
(PhD candidate, TU/e, Philosophy)
PhD expected June 2025
Yeji’s work focuses on tackling the new demarcation problem (i.e. value demarcation) in science. Specifically, Yeji is interested in how we can evaluate machine learning models, theoretically and practically, for whether the values they incorporate maintain epistemic excellence and fairness and how we might justify these values compared to other values.
Recent publications:
Streppel, Yeji forthcoming “Demarcating value demarcation in Machine learning” Philosophy of Science.
Former members:
Philippe Verreault-Julien
Currently an AI Ethics consultant for The Commission on Ethics in Science and Technology; Government of Quebec
Lab Sponsors
Kaush Kalidindi
(PhD candidate, TU/e, Philosophy)
PhD expected 2027
Kaush is interested in how methodologies in philosophy of science can shed light on questions concerning the ethical use of AI, as well as more artistic and creative use of machine learning technologies. Specifically, Kaush is interested in how machine learning models should be evaluated based on their process of construction, instead of their outputs. He is also interested in how epistemic communities might re-appropriate technologies to combat various forms of epistemic injustice. He is an active member in the Art track in ESDiT, where he actively collaborates with artists working with generative AI technologies.
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