Philosophy of Machine Learning Conference
A philosophy conference bringing together researchers from across disciplines to explore foundational epistemological and ethical questions concerning machine learning.
📍LMU main building, Room ...
Geschwister-Scholl-Platz 1, 80539 Munich
Conference organisers:
Timo Freiesleben, Thomas Grote, Tom Sterkenburg, and Kate Vredenburgh
PhilML is an annual conference dedicated to the philosophy of machine learning. It addresses foundational epistemological, ethical, and social questions concerning machine learning from the perspective of analytic philosophy. The conference welcomes both (1) work that applies philosophical concepts and methods to gain insight into machine learning and (2) work that critically reflects on the philosophical and ethical implications of machine learning research. To foster close and productive exchange, PhilML brings together philosophers and philosophically inclined machine learning researchers, with an openness to engaging directly with scientific and mathematical details.
The main conference will take place from October 7–9, preceded by a graduate workshop on October 6. We invite submissions of extended abstracts for both the main conference and the graduate workshop.
We are excited to announce that the speakers at this year's conference will include:
TBA.
See the conference program for more information.
Reflections on key topics such as learning, benchmarking, robustness, explanation, causality, trust, transparency, reliability, and fairness.
Novel considerations raised by foundation models e.g., agency, alignment, authorship, mechanistic interpretability, safety, or homogenization.
Issues arising at the intersection of machine learning and public policy, e.g. public services, resource allocation, or climate policy.
Implications of machine learning for the sciences or their methodology, e.g. physics, cognitive science, biology, social science, or medicine.