Philosophy of Science meets Machine Learning

Auf dem Bild sind eine menschliche Hand und eine Roboterhand zu sehen, die sich zueinander hinstrecken. Die Fingerspitzen berühren sich fast. Das Bild ist angelehnt an Michelangelos Die Erschaffung Adams, wo Gott und Adam ihre Hände in gleicher Weise zueinander hinstrecken.
Conference for Junior Scholars

November 9th-12th, 2021

Venue: Alte Aula und Konferenzsaal des Max Planck-Instituts für Intelligente Systeme Tübingen

Starting Time:  13:00 Uhr

Organisation: Thilo Hagendorff, Thomas Grote, Eric Raidl (Ethics and Philsophy Lab des Exzellenzclusters "Maschinelles Lernen für die Wissenschaft", Universität Tübingen)

Contact and registration


Machine learning (ML) does not only transform businesses and the social sphere, it also fundamentally transforms science and scientific practice. The workshop focuses on that latter issue. It aims to discuss whether and how ML transforms the process of scientific inquiry. For this, it sets out to analyse the field of ML through the lenses of philosophy of science, epistemology, ethics and cognate fields such as sociology of science.



Day 1: November 9th, Alte Aula
13:00 Registration & Coffee
13:50 Short Introduction
14:00 -14:50 Gregory Wheeler – “Discounting Desirable Gambles”
15:00 -15:40 Vlasta Sikimic – “Algorithmic grant review: benefits and limitations”
15:50-16:40 Emily Sullivan – “Stopping the Opacity Regress”
16:40-17:00 Coffee & Snacks
17:00-17:50 Bob Williamson – “(Un)stable facts, and (missing) chains of reference in machine learning”
  Evening activities /Dinner


Day 2: November 10th, Alte Aula
9:00-9:50 Carlos Zednik – “The Exploratory Role of Explainable Artificial Intelligence”
10:00-10:40 Moritz Renftle et al. – “Evaluating the Effect of XAI on the Understanding of Machine Learning Models”
10:40-11:20 Timo Freiesleben – “To Explain and to Predict - Explanatory Machine Learning Models in Science”
11:20-11:40 Coffee & Snacks
11:40-12:30 Carina Prunkl – “Governance from within: opportunities and responsibilities facing the AI research community”
12:30-14:00 Extended lunch break
14:00-14:50 Jon Williamson – “Evidential Pluralism and Explainable AI”
15:00-15:40 Oliver Buchholz – “Towards a Means-End Account of XAI”
15:40-16:00 Break
16:00-16:40 Koray Karaca – “Inductive Risk and Values in Machine Learning”
16:40-17:30 Lena Kästner – “Grasping Psychopathology: On Complex and Computational Models”
  Informal discussion / Dinner


Day 3: November, 11th, MPI-IS
9:30-10:10 Benedikt Hoeltgen – “Causal Variable Selection Through Neural Networks”
10:10-10:50 Daniela Schuster – “Suspension of Judgment and Explainable AI”
10:50-11:20 Coffee & Snacks
11:20-12:10 Anouk Barberousse – “Can the Concept of Scientific Knowledge be Transformed by Machine Learning?”
12:10-14:00 Extended lunch break
14:00-14:40 Giorgio Gnecco et al. – “Simple Models in Complex Worlds: Occam`s Razor and Statistical Learning Theory” (Online)
14:40-15:20 Atoosa Kasirzadeh – “Kinds of Explanation in Machine Learning” (Online)
15:20-15:50 Coffee Break
15:50-16:30 Tim Räz – “Understanding Machine Learning for Empiricists”
16:30- 17:20 Alex Broadbent – “Predictive Investigation and Deep Learning”
  Informal discussion / Dinner


Day 4: November 12th, MPI-IS
9:30-10:10 Mario Günther – “ How to Attribute Beliefs to AI Systems?” (Online)
10:10-10:50 Dilectiss Liu – “Epistemic Opacity Does Not Undermine the Epistemic Justification of Machine Learning Models”
11:00-11:50 Kate Vredenburgh – “Against Rational Explanations”
11:50-12:20 Coffee Break
12:20-13:00 Roundtable Farewell