Ethics and Epistemology of Machine Learning

The Ethics and Epistemology of Machine Learning (EEML) research group is part of the Cluster of Excellence Machine Learning for Science at the Universty of Tübingen.

We are a mixed-background team of researchers working at the intersections of machine learning, philosophy of science, formal learning theory, causality, and law.

Featuring

Konstantin Genin / Sebastian Zezulka / Raysa Benatti / Mykhailo Bogachov

Round-cropped outdoor night picture of the city of Tubingen

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.

News

📚PhilML reading group continues in summer term

The PhilML reading group continous in the summer term! We discuss 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.

The next session of will take place on Thursday, 22.05., 1-2 pm in the meeting room on the second floor of the AI research building (Maria-von-Linden-Str. 6). We will discuss

Sterkenburg, Tom. "The meta-inductive justification of induction." Episteme 17.4 (2020): 519-541.

All events can be found on talks.tue.ai.

Organized by Ben Höltgen and Sebastian Zezulka.

08.05.2025

📢 Final CfR PhilML2024 (11.-13.09.2024)

Call for Registration for the fourth Philosophy of Science meets Machine Learning conference: PhilML24.

The conference will take place from 11-13 September 2024 in Tübingen, Germany.

Please register by 02 September. You can register for the conference here.

We are pleased to present the program for the conference.

See the conference webpage for more information.

26.08.2024

New Publications 🎉 EEML @FAccT24

We're happy to share that EEML was represented at this year's ACM FAccT conference with two papers and a tutorial session:

Raysa Benatti, Fabiana Severi, Sandra Avila, and Esther Luna Colombini. 2024. Gender Bias Detection in Court Decisions: A Brazilian Case Study. In Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency (FAccT '24). Association for Computing Machinery, New York, NY, USA, 746–763. https://doi.org/10.1145/3630106.3658937
Sebastian Zezulka and Konstantin Genin. 2024. From the Fair Distribution of Predictions to the Fair Distribution of Social Goods: Evaluating the Impact of Fair Machine Learning on Long-Term Unemployment. In Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency (FAccT '24). Association for Computing Machinery, New York, NY, USA, 1984–2006. https://doi.org/10.1145/3630106.3659020

Raysa Benatti also gave a tutorial session: "Should I disclose my dataset? Legal and ethical considerations for researchers dealing with court documents".

24.06.2024

CfA PhilML2024 (11.-13.09.2024)

As the co-organizers of the fourth Philosophy of Science Meets Machine Learning conference (PhilML‘24), taking place on September 11-13, 2024 at the University of Tübingen, we would like to invite the submission of extended abstracts.

Since 2021, PhilML has brought together scientifically engaged philosophers with machine learners to address foundational issues raised by developments in ML research. Submissions are invited from all philosophical subfields, including philosophy of science, mind, ethics, epistemology, and political philosophy, as well as foundational and philosophical submissions from machine learners.

Submissions, due May 24, should consist of an anonymised extended abstract (750 words, not including references), along with a cover sheet with your name, email address, and institutional affiliation. It should be sent as an attachment to philml2024@gmail.com.

See the conference webpage for more information.

08.04.2024

📚 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.

01.02.2024

EEML @NeurIPS23 Workshop "Algorithmic Fairness through the Lens of Time"

We are looking forward to presenting our paper "Performativity and Prospective Fairness" at the AFT 2023 Workshop at NeurIPS this year.

01.12.2023

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

Hello world.

We're online now. Yay!

Sep. 3rd, 2023

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