Books

• Explaining Artificial Intelligence. From Epistemological Foundations to Practical Consequences, forthcoming with De Gruyter.
  [book]
  
  

Articles in peer-reviewed journals

• Evaluating GPT-4's Ability to Generate Informed Consent Material for Genetic Testing (with Eirini Petrou, Kelly Ormond, Dominik Stammbach, Elliott Ash, and Effy Vayena), accepted for publication at npj Artificial Intelligence.
  
  
• XAI: On Explainability and the Obligation to Explain (with Karoline Reinhardt), Digital Society, 2025.
  [journal]
  
  
• A Means-End Account of Explainable Artificial Intelligence, Synthese, 2023.
  [preprint] [journal]
  
  
• The Deep Neural Network Approach to the Reference Class Problem, Synthese, 2023.
  [preprint] [journal]
  
  
• Predicting and Explaining with Machine Learning Models: Social Science as a Touchstone (with Thomas Grote), Studies in History and Philosophy of Science, 2023.
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• Analyzing the Relationship between Physicians' Experience and Surgery Duration (with Christopher Haager, Katja Schimmelpfeng, Jan Schoenfelder, and Jens Brunner), Operations Research for Health Care, 2023.
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• A Falsificationist Account of Artificial Neural Networks (with Eric Raidl), The British Journal for the Philosophy of Science, 2022.
  [preprint] [journal]
  
  

Chapters in edited volumes

• AI Ethical Principles: The Debate (with Marcello Ienca and Effy Vayena), in: Floridi, L. and Taddeo, M. (eds.): A Companion to Digital Ethics. Oxford: Wiley-Blackwell, 2025.
  [chapter]
  
  
• Maschinelles Lernen in der Wissenschaft, in: Noller, J. and Reinhardt, K. (eds.): Handbuch Philosophie der Digitalität. Heidelberg: Metzler, 2025.
  [preprint] [chapter]
  
  
• Epistemology and Politics of AI (with Karoline Reinhardt), in: Hähnel, M. and Müller, R. (eds.): A Companion to Applied Philosophy of AI. Oxford: Wiley-Blackwell, 2025.
  [chapter]
  
  
• Digital Ethics (with Robert Ranisch and Effy Vayena), forthcoming in: Sugarman, J., and Sulmasy, D. P. (eds.): Methods in Medical Ethics. Scholarship, Practice and Policy in Bioethics. Washington, D.C.: Georgetown University Press.
  
  
• Machine Learning in Public Health and the Prediction-Intervention Gap (with Thomas Grote), forthcoming in: Durán, J. and Pozzi, G. (eds.): Philosophy of Science for Machine Learning: Core Issues and New Perspectives. Cham: Synthese Library.
  [preprint]
  
  

Work in progress

• A paper about the curve-fitting problem in light of machine learning, under review.
  
  
• A paper on AI in medicine (with Alessandro Blasimme), under review.
  
  
• Shaking up the Dogma: Solving Trade-offs without (Moral) Values in Machine Learning (with Thomas Grote), under review.
  [preprint]
  
  

Theses

• Artificial Neural Networks and the Reference Class Problem, thesis for the degree M. A. in Philosophy, 2020.
  [thesis]
  
  
• Uncertainty and the Business Cycle, thesis for the degree M. Sc. in Economics and Finance, 2020.
  [thesis]