Philosophy of Science Meets Machine Learning conference (PhilML‘24)

We invite you to the fourth Philosophy of Science Meets Machine Learning conference (PhilML‘24), taking place on September 11-13, 2024 at the University of Tübingen. Please register at https://sites.google.com/view/philml-tuebingen/registration?authuser=0 if you want to attend the conference.

Since 2021, PhilML has brought together scientifically-engaged philosophers with machine learners to address foundational issues raised by developments in ML research. Submissions are invited from all philosophical subfields, including philosophy of science, mind, ethics, epistemology and political philosophy, as well as foundational and philosophical submissions from machine learners. 

The workshop’s central topics include:

(i) Reflections on key topics such as learning, reliability, causal inference, robustness, explanation, trust, transparency and understanding.  

(ii) Implications of machine learning for the sciences, e.g. physics, cognitive science, biology, psychology, social science or medicine. 

(iii) Implications of machine learning for scientific methodology, e.g. model-building and model selection, design of experiments, conceptual engineering. 

(iii) Issues arising at the intersection of machine learning and public policy, e.g. risk assessment, resource allocation, climate and energy policy, the provision of public services. 

(iv) Novel considerations raised by foundation models e.g., authorship, latent representation, or nativism/empiricism.

As in the last years, we again have an amazing line-up of invited speakers:

- Stefan Buijsman (Delft)
- Molly Crockett (Princeton)
- Julia Haas (DeepMind)
- Dominik Janzing (Amazon)
- Gabbrielle Johnson (Claremont)
- Brent Mittelstadt (Oxford)
- Alexander Tolbert (Emory)
- Ana-Andreea Stoica (MPI-IS)

If you have questions, please email: [email protected]