Machine learning methods have become a mainstay in the tool-kit of various scientific disciplines. After a successful initial workshop in 2021, PhilML 2022 offers an opportunity to explore whether and how exactly recent developments in the field of machine learning potentially transform the process of scientific inquiry. For this, it sets out to analyse the field of machine learning through the lenses of philosophy of science – including cognate fields such as epistemology, ethics, or STS. In addition, we are also interested in contributions from machine learning researchers/scientists, addressing foundational issues of their research. Similar to the previous workshop, we try to bring together philosophers from different backgrounds (from formal epistemology to the study of the social dimensions of science) and machine learning researchers. 

The workshop's central topics are:

• A critical reflection on key-concepts, such as ‘learning’, ‘robustness’, ‘explanation’, ‘measurement’, or ‘causation’.

• The implications of machine learning for the special sciences, e.g., cognitive science, biology, social science or medicine.

• The ethics of machine learning-driven science, e.g., the moral responsibilities of researchers, ethical issues in model evaluation or issues at the intersection of science and policy. 

• Social aspects of machine learning-driven science, e.g., the impact of funding structures on research.

The workshop is organised by the Cluster of Excellence ‘Machine Learning: New Perspectives for Science’ at the University of Tübingen.