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: thomas.grote@uni-tuebingen.de
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.
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 |
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 |
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 |
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 |