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Hi, I'm Conny — a PhD candidate in Philosophy at the University of Pittsburgh, advised by Bob Batterman. My dissertation asks how scientists can make use of AI models they can't fully see inside of.

My interests are in general philosophy of science, the philosophy of AI, and the philosophy and history of mathematics. More specifically, I work on questions about AI models used in science, the issue of generalization in deep learning, and trust and values in science.

"DLMs in Science: Epistemic Legitimacy without Representation"

In my dissertation, I argue that deep learning models (DLMs) pose particular epistemic risks given their opacity, and that these risks threaten their legitimate deployment in science. However, I argue that the literature commonly misunderstands this issue by treating them like scientific models and demanding that they become fine-grained representational models. I argue that their epistemic profile is better understood in comparison to scientific instruments. Opacity, so understood, is not a failure of representation but a challenge to validation. I propose a framework of external validation that enables us to assess whether the patterns a DLM exploits track the phenomenon of interest versus an artifact in the training data. An important claim in my thesis is that internal transparency and representation is not necessary for a DLM to contribute to scientific explanations.

My dissertation consists of four chapters: (1) The Legitimacy Gap, a three-level opacity analysis; (2) Against Representation, a critique of DLMs-as-representational-scientific-models; (3) A Diagnostic Taxonomy of DLM deployments; (4) Workflow-Conferred Legitimacy, my positive account.

[Add courses, teaching philosophy, and syllabi or evaluations here.]

"Deep Learning Models in Science: Reliability Without Representation"
My job-market paper — a single-arc defense of a reliability approach, anchored by AlphaFold. Available on request.

[CV coming soon.]

cok22 [at] pitt [dot] edu