Philosophy of Science meets Machine Learning

Registration:

We opened the registration to our Philosophy of Science meets Machine Learning (PhilML) conference. You can register under sites.google.com/view/philml-tuebingen/registration

Aim:

The PhilML conference and workshop sets out to analyze the field of machine learning through the lens of philosophy of science, including cognate fields such as epistemology and ethics. 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:

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

For the full programme, check out: https://sites.google.com/view/philml-tuebingen/program?authuser=0 

Topics:

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.