Welcome

Ben Jantzen (Virginia Tech): Machines and Ontological Induction

Kate Vredenburgh (LSE): Against Decision Thresholds

Donal Khosrowi (Hannover): Generalization in Machine Learning: A Plea for Better Theory

Lunch Break

Tim Räz (Bern): Machine Learning and the Quest for Objectivity in Climate Model Parameterization

Winnie Ma (KCL): Profiling Patients

Coffee Break/Snacks

Heather Champion (Rotman Institute): A Transferrable Conceptual Toolbox for Characterizing Novelty with Machine-led Exploratory Experiments

Cameron Buckner (Houston): On Measures Relating Representations in DNNs to Representations in the Brain

Dinner at Caro’s

Coffee

Uljana Feest (Hannover): Big Data and Machine Learning in the Measurement of Personality Traits

David Watson (KCL): Probing Errors with Explainable Artificial Intelligence (joint work with D. Mayo)

Hong Yu Wong (Tübingen): Varieties of Intelligence?

Lunch Break

Jan-Willem Romeijn (Groningen): Machine Learning, or: The Return of Instrumentalism

Brandon James Ashby (York): Machine Learning Models of High-Level Vision: On the Format and Character of Category Perception

Coffee Break/Snacks

Lily Hu (Yale): TBA

Zachary Lipton (CMU): Induction, Identification, Interpolation, and Extrapolation in Machine Learning

Dinner at Freistil

Coffee

Emily Sullivan (Eindhoven): Idealization in Machine Learning and XAI

Bojana Grujicic (UCL): Deep Neural Networks as Mechanistic Explanations of Object Recognition: In Search of the Explanans (CANCELLED)

Rabanus Derr (Tübingen): Fairness and Randomness in Machine Learning: Statistical Independence and Relativization

Lunch Break

Frederick Eberhardt (Caltech): Continuous Causal Macro Variables

Katie Creel (Northeastern): A Molineux Problem for Automated Science

Tom Sterkenburg (LMU): Machine Learning and the Philosophical Problem of Induction

Closing Words and Drinks