CFP: PhilML'26 Graduate Conference

Deadline extended by one week! 

We are pleased to announce the latest iteration of the PhilML graduate workshop, on October 6, 2026, at the LMU Munich.  

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. 

PhilML now includes a PhD student workshop the day before the main conference. PhD students are welcome to apply with abstracts to both the PhilML graduate workshop and the PhilML workshop. We are soliciting abstracts of up to 1,000 words.

Abstracts can be submitted through Oxford Abstracts: https://app.oxfordabstracts.com/stages/82826/submitter. Abstracts must be submitted by July 16, 2026. 

We are excited to announce that Emily Sullivan will deliver the keynote talk. 

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).