We are pleased to announce the latest iteration of PhilML, from October
7-9 2026, at the LMU Munich.
https://sites.google.com/view/philmlconference/home
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.
Central topics that will be covered at the conference include:
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.
The conference has space for a number of contributing speakers. We are
soliciting abstracts of up to 1,000 words. Abstracts can be submitted
through Oxford Abstracts:
https://app.oxfordabstracts.com/stages/82713/submitter . Abstracts must
be submitted by July 1, 2026. Abstracts from scholars at all career
stages are welcome, including PhD students.
We are excited to announce that the speakers at this year’s conference
will include: Sara Aronowitz, Anne-Laure Boulesteix, Ali Boyle,
Annemarie Friedrich, Konstantin Genin, Lily Hu, Christoph Kern, and
Anders Søgaard.
There will also be a PhD student workshop on October 6, 2026. The call
for abstracts will be announced in due course.
This iteration of PhilML is funded by the Munich Center for Machine
Learning (MCML), Konrad Zuse School of Excellence in Reliable AI
(relAI), and the Munich Center for Mathematical Philosophy (MCMP).
