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Machine Learning: Prediction Without Explanation?

Machine Learning: Prediction Without Explanation?
type of event:

Workshop

place:
ITAS, Karlstr. 11, 76133 Karlsruhe
date:

17.02.20 - 18.02.20

Description

"Machine Learning: Prediction Without Explanation?" is a 2-day workshop taking place from 17 to 18 February 2020 at the Karlsruhe Institute for Technology (KIT), Germany. It aims to bring together philosophers of science and scholars from various fields using Machine Learning techniques, to reflect on the changing face of science in the light of Machine Learning's constantly growing use. This workshop is organized by the project “The Impact of Computer Simulations and Machine Learning on the Epistemic Status of LHC Data” within the interdisciplinary, DFG/FWF-funded research unit “Epistemology of the LHC”.

Over the last decades, Machine Learning techniques have gained prominence in various areas of science. However, Machine Learning largely aim at predictions and does not seem to provide explanations for these, at least not in the same sense as predictions from theories or models do. Depending on the area of application, explanations may be desired or even necessary though. In this workshop, we want to address the complex of questions regarding scientific explanation that arise from this observation. These include, but are not restricted to:

  • Will future science favor prediction above explanation?
  • What methods are available to use Machine Learning results for explanations?
  • What is the nature of these explanations?
  • Does machine learning introduce a shift from the classical scientific explanation towards a statistical interpretation of explanation?

Schedule

17.02.2020
12:00 – 12:30  Welcome
12:30 – 13:45  Stefan Hinz – Automatic understanding of large scale imagery - from semantic networks to deep learning (and back?)
13:45 – 14:30  Tom Sterkenburg - On explaining the success of machine learning methods
14:30 – 14:45  Coffee break
14:45 – 15:30  Timo Freiesleben - Counterfactual Explanations & Adversarial Examples
15:30 – 16:45  Annette Zimmermann - Opacity, Explainability, and Justification in Machine Learning
16:45 – 17:30  Florian Boge & Paul Grünke – Machine Learning Opacity and Explanations in High Energy Physics
18.02.2020
09:00 – 10:15  Erwin Zehe, Uwe Ehret & Ralf Lorenz – Machine learning in environmental sciences - beyond causality or just brute force?
10:15 – 11:00  Thomas Grote - Evidence, Uncertainty and the Integration of Machine Learning into Medical Practice
11:00 – 12:15  Johannes Lenhard - The History of Mathematization and a New Culture of Prediction
12:15 – 13:15  Lunch break
13:15 – 14:00  Sergey Titov - Statistical relevance explanation models and modern methods of interpretable machine learning
14:00 – 14:45  Maël Pégny – The Relations Between Scientific and Pedagogical Explainability: Lessons from Algorithmic Aids to Decision-Making
14:45 – 15:15  Coffee break
15:15 – 16:30  Andreas Kaminski – The kind of reasons and the type of explanation in technoscience
16:30 – 17:00  Final discussion

Organization & Contact

This workshop is organized by the project “The Impact of Computer Simulations and Machine Learning on the Epistemic Status of LHC Data” within the interdisciplinary, DFG/FWF-funded research unit “Epistemology of the LHC”. For further information, please contact the organizers: