September 11 - 13, 2024

PhilML'24

Tübingen (Germany)

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 toolkit 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.

The PhilML conference and workshop are organized jointly by the Ethics and Philosophy Lab and the Epistemology and Ethics of Machine Learning group at the Tübingen Cluster of Excellence "Machine Learning".

Kate Vredenburgh (LSE): "The limits of explainability for reducing algorithmic discrimination."

Public keynote lecture at the graduate workshop:
Tuesday, September 10, 2-3 pm
in the lecture hall of the AI Research Building,
Maria-von-Linden-Str. 6, Tübingen.

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

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).

See the conference program for more information.

The workshop’s central topics include

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.