September 11 - 13, 2024
PhilML'24
Tübingen (Germany)
📍AI Research Building, Lecture Hall
Maria-von-Linden-Str. 6, 72076 Tübingen
Conference organisers: Markus Ahlers, Raysa Benatti, Heather Champion,
Timo Freiesleben, Konstantin Genin, Thomas Grote, Sebastian Zezulka
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 how recent developments in machine learning change the process of scientific research. The main conference will take place from September 11-13 and will be preceded by a one-day graduate workshop on September 10. We invite submissions of extended abstracts both for the conference and the graduate workshop. Submissions for the conference are due May 24, for the graduate workshop June 15.
The PhilML conference and workshop are organised jointly by the Ethics and Philosophy Lab and the Epistemology and Ethics of Machine Learning group at the Tübingen Cluster of Excellence "Machine Learning".
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. 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 excited to announce that the speakers at this year's conference will include:
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).
The workshop’s central topics include
Reflections on key topics such as learning, reliability, causal inference, robustness, explanation, trust, transparency, and understanding.
Implications of machine learning for the sciences, e.g. physics, cognitive science, biology, psychology, social science, or medicine.
Implications of machine learning for scientific methodology, e.g. model-building and model selection, design of experiments, conceptual engineering.
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.
Novel considerations raised by foundation models e.g., authorship, latent representation, or nativism/empiricism.
