Ethics and Epistemology of Machine Learning
It is widely acknowledged that questions of scientific methodology depend on ethical ones. If an experiment is unethical, it ought not to be performed. If an algorithm is unfair, it ought not to be implemented. From this perspective, ethics responds to methodological advances by rushing to install new guard-rails. But ethical questions also depend on methodological ones.
Whether an experiment is ethical depends on whether similarly reliable inferences could be made from non-experimental data. Whether an algorithm is fair depends on how well it manages delicate tradeoffs between competing explications of fairness. The answers to these questions typically turn on methodological ones and -- more often than not -- these are both highly technical and hotly contested. From this perspective, methodological advances lead inevitably to ethical ones.
The goal of the "Ethics and Epistemology" research group is to work these problems from both sides: to approach methodological issues with an eye to their social consequences and to approach ethical issues with an eye to methodological solutions.
PhilML reading group continues in 2024.
The PhilML reading group continous in 2024! The group discusses current topics in the philosophy of machine learning with a special focus on the philosophy of science. All interested students and researchers are welcome to join. Participants are kindly asked to read the respective paper in advance.
All events can be found on talks.tue.ai.
Organized by Timo Freiesleben, Ben Höltgen, and Sebastian Zezulka.
PhilML'23 is coming up!
The third edition of our Philosophy of Science meets Machine Learning conferece is taking place in Tübingen, Germany, between September 12th and 14th. You can check all the details here.
Sep. 6th, 2023
We're online now. Yay!
Sep. 3rd, 2023