PhilML'21

The first PhilML'21 conference took place in Tübingen from 09.-12.November 2021 at the Alte Aula and the Max Planck Institute for Intelligent Systems. 

Workshop organisers: Thomas Grote, Thilo Hagendorff, Eric Raidl.

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

09:00 - 09:50   Carlos Zednik - "The Explanatory 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 Alex Broadbent - "Predictive Investigation and Deep Learning"

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 Selectrion 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 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 Statistcal Learning Theory"

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 Carina Prunkl - "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