Philosophy of Science meets Machine Learning

Machine learning methods have become a mainstay in the tool-kit of various scientific disciplines. For the fourth time, the PhilML'24 conference offers the opportunity to explore whether and how recent developments in machine learning change the process of scientific research.  

For this purpose, it sets out to analyse the field of machine learning through the lens of philosophy of science, including cognate fields such as epistemology and ethics. In addition, we are also interested in contributions from machine learning researchers and scientists, addressing foundational issues of their research. Similar to the previous workshops, we bring together philosophers from different backgrounds from formal epistemology to the study of the social dimensions of science and machine learning researchers.

Speakers

We are happy to announce that the speakers at this year's workshop will among others include

Dominik Janzing (Amazon Research Tübingen), Molly Crockett (Princeton University), Julia Haas (DeepMind), Ana-Andreea Stoica (MPI Tübigen), Alexander Tolbert (Emory University), Gabbrielle Johnson (Claremont McKenna College) and Stefan Buijsman (TU Delft).

The workshop’s central topics include

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

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

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

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

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