{"id":359,"date":"2023-12-11T09:39:47","date_gmt":"2023-12-11T09:39:47","guid":{"rendered":"https:\/\/udnn.tu-dortmund.de\/?page_id=359"},"modified":"2025-10-06T11:16:44","modified_gmt":"2025-10-06T11:16:44","slug":"ws-epi-issues","status":"publish","type":"page","link":"https:\/\/udnn.tu-dortmund.de\/index.php\/activities\/ws-epi-issues\/","title":{"rendered":"Workshop: Epistemological Issues of Machine Learning in Science"},"content":{"rendered":"\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-28f84493 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1568\" height=\"459\" src=\"https:\/\/udnn.tu-dortmund.de\/wp-content\/uploads\/2023\/03\/bckgrnd.jpg\" alt=\"\" class=\"wp-image-163\" srcset=\"https:\/\/udnn.tu-dortmund.de\/wp-content\/uploads\/2023\/03\/bckgrnd.jpg 1568w, https:\/\/udnn.tu-dortmund.de\/wp-content\/uploads\/2023\/03\/bckgrnd-300x88.jpg 300w, https:\/\/udnn.tu-dortmund.de\/wp-content\/uploads\/2023\/03\/bckgrnd-1024x300.jpg 1024w, https:\/\/udnn.tu-dortmund.de\/wp-content\/uploads\/2023\/03\/bckgrnd-768x225.jpg 768w, https:\/\/udnn.tu-dortmund.de\/wp-content\/uploads\/2023\/03\/bckgrnd-1536x450.jpg 1536w\" sizes=\"auto, (max-width: 1568px) 100vw, 1568px\" \/><\/figure>\n<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-group is-layout-constrained wp-block-group-is-layout-constrained\">\n<h2 class=\"wp-block-heading has-text-align-center\">Workshop:<br> Epistemological Issues of Machine Learning in Science<\/h2>\n\n\n\n<p><strong>Date: <\/strong>27.\u201328.02.2024<\/p>\n\n\n\n<p><strong>Place:<\/strong> Chaudoire Pavillon, TU Dortmund University<\/p>\n\n\n\n<p><strong><a href=\"https:\/\/udnn.tu-dortmund.de\/wp-content\/uploads\/2024\/02\/BookletEpistIssuesMLSci.pdf\">Conference booklet<\/a><\/strong><\/p>\n\n\n\n<p><strong><a href=\"https:\/\/udnn.tu-dortmund.de\/wp-content\/uploads\/2023\/12\/Epistemological-Issues-of-Machine-Learning-in-Science-Poster-1.pdf\">Poster<\/a><\/strong><\/p>\n\n\n\n<p><strong>Description<\/strong><\/p>\n\n\n\n<p>With impressive advances in Machine Learning (ML) and particularly Deep Learning, Artificial Intelligence is currently taking science by storm. This workshop brings together top scientists and philosophers working on fundamental issues connected to the use of Machine Learning in science. The workshop marks the launch of the DFG-funded Emmy Noether Group <em>UDNN: Scientific Understanding and Deep Neural Networks<\/em>, and is co-organized with the <em>Lamarr Institute for Machine Learning and Artificial Intelligence<\/em> and co-funded by the <em>Department for Humanities and Theology<\/em> at TU Dortmund University.<\/p>\n\n\n\n<p>Topics include, but are not restricted to:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The relation between prediction and discovery on the one hand, and explanation and understanding on the other, in fields of science that heavily rely on ML methods<\/li>\n\n\n\n<li>The key issues in identifying genuine discoveries and stable predictions by ML systems<\/li>\n\n\n\n<li>Core conceptions of \u201cexplanation\u201d involved in the field of eXplainable AI (XAI), and their relation to philosophical theories of understanding and explanation<\/li>\n\n\n\n<li>Present limitations associated with ML\u2019s predictive power and what may be needed to overcome them<\/li>\n\n\n\n<li>The connection between ML and traditional scientific means for prediction and discovery, such as theories, models, and experiments<\/li>\n\n\n\n<li>Our present understanding of ML itself and its limitations&nbsp;<\/li>\n<\/ul>\n\n\n\n<p><strong>Speakers<\/strong><\/p>\n\n\n\n<p><em>Life Sciences<\/em><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>J\u00fcrgen Bajorath (University of Bonn)<\/li>\n\n\n\n<li>Axel Mosig (Ruhr University Bochum)<\/li>\n<\/ul>\n\n\n\n<p><em>Machine Learning Theory<\/em><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>M. Klopotek (University of Stuttgart)<\/li>\n\n\n\n<li>Marie-Jeanne Lesot (Sorbonne Universit\u00e9)<\/li>\n\n\n\n<li>David Watson (King\u2019s College London)<\/li>\n<\/ul>\n\n\n\n<p><em>Philosophy <\/em><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Katie Creel (Northeastern University)<\/li>\n\n\n\n<li>Brigitte Falkenburg (TU Dortmund)<\/li>\n\n\n\n<li>Konstantin Genin (University of T\u00fcbingen)<\/li>\n\n\n\n<li>Lena K\u00e4stner (University of Bayreuth)<\/li>\n\n\n\n<li>Henk de Regt (Radbout University Nijmegen)<\/li>\n\n\n\n<li>Eva Schmidt (TU Dortmund)<\/li>\n\n\n\n<li>Tom Sterkenburg (LMU Munich)<\/li>\n<\/ul>\n\n\n\n<p><em>Physics \/ Astronomy<\/em><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Dominik Els\u00e4sser (TU Dortmund)<\/li>\n\n\n\n<li>Michael Kr\u00e4mer (RWTH Aachen)<\/li>\n\n\n\n<li>Mario Krenn (Max Planck Institute for the Science of Light)<\/li>\n\n\n\n<li>Wolfgang Rhode (TU Dortmund)<\/li>\n\n\n\n<li>Christian Zeitnitz (BU Wuppertal)<\/li>\n<\/ul>\n\n\n\n<p>Registration is free but places are limited. To register, please send an E-mail to <a href=\"mailto:udnn.fk14@tu-dortmund.de\">udnn.fk14@tu-dortmund.de<\/a> until <strong>January 15, 2024 <\/strong>including your name, institution. A small number of attendees will be able to join the conference dinner on the 27th on a dutch-treat basis. If you want to join the dinner, please indicate this in your registration.&nbsp;<\/p>\n\n\n\n<h1 style=\"font-size: 1.5rem\">Schedule<\/h1>\n\n\n\n<style>\ntable, th, td {\n  border: 1px solid black;\n  border-collapse: collapse;\n}\n<\/style>\n\n\n<h1 style = \"font-size: 15px\"> \n<table style=\"width:100%\">\n\n<tr>\n    <th style=\"width:20%\">Day 1<\/th>\n    <th style=\"width:30%\"> <\/th>\n    <th style=\"width:20%\">Day 2<\/th>\n    <th style=\"width:30%\"> <\/th>\n<\/tr>\n \n<tr>\n    <td>09:00\u201309:15<\/td>\n    <td style=\"color:rgb(255, 100, 70); margin: 0; padding: 0;\">Arrival + Coffee<\/td>\n    <td>09:00\u201309:15<\/td>\n    <td style=\"color:rgb(255, 100, 70); margin: 0; padding: 0;\">Arrival + Coffee<\/td>\n<\/tr>\n<tr>\n    <td>09:15-09:20<\/td>\n    <td>Opening (FJB)<\/td>\n    <td>09:15 &#8211; 10:00<\/td>\n    <td><em>From the fair distribution of predictions to the fair distribution of \n    social goods: evaluating the impact of fair machine learning on long-term \n    unemployment <\/em><br> <span style=\"color: rgb(70, 100, 255)\">Konstantin Genin, \n    T\u00fcbingen<\/span><\/td>\n<\/tr>\n\n<tr>\n    <td>09:20-10:05<\/td>\n    <td><em>Can machines acquire scientific understanding?<\/em><br> <span style=\"color: \n    rgb(70, 100, 255)\">Henk de Regt, Nijmegen<\/span><\/td>\n    <td>10:00 &#8211; 10:45<\/td>\n    <td><em> Explainable AI and trustworthy AI: a relation to discuss <\/em><br><span \n    style=\"color: rgb(70, 100, 255)\">Marie-Jeanne Lesot, Paris<\/span><\/td>\n<\/tr>\n<tr>\n    <td>10:05\u201310:50<\/td>\n    <td><em> Richness revisited: clustering and PAC learnability <\/em><br> <span \n    style=\"color: rgb(70, 100, 255)\">David Watson, London<\/span><\/td>\n    <td>10:45 &#8211; 11:00<\/td>\n    <td style=\"color:rgb(255, 100, 70); margin: 0; padding: 0;\">Coffee<\/td>\n<\/tr>\n<tr>\n    <td>10:50\u201311:05<\/td>\n    <td style=\"color:rgb(255, 100, 70); margin: 0; padding: 0;\">Coffee<\/td>\n    <td>11:00 &#8211; 11:45<\/td>\n    <td><em> Towards an artificial muse for new ideas in science<\/em><br> <span \n    style=\"color: rgb(70, 100, 255)\">Mario Krenn, Erlangen<\/span><\/td>\n<\/tr>\n<tr>\n    <td>11:05\u201311:50<\/td><td><em> Occam&#8217;s razor in machine learning<\/em> <br> <span \n    style=\"color: rgb(70, 100, 255)\">Tom Sterkenburg, Munich<\/span><\/td>\n    <td>11:45 &#8211; 12:15<\/td><td><em> Navigating the black box: Understanding particle \n    physics with deep neural networks and explainable artifical intelligence<\/em> \n    <br><span style=\"color: rgb(70, 100, 255)\">Frauke Stoll, Dortmund<\/span><\/td>\n<\/tr>\n<tr>\n    <td>11:50\u201312:20<\/td>\n    <td><em>A new pathway: From objectual to explanatory understanding with \n    AlphaFold2 <\/em><br> <span style=\"color: rgb(70, 100, 255)\">Annika Schuster, \n    Dortmund<\/span><\/td>\n    <td>12:15\u201313:15<\/td>\n    <td style=\"color:rgb(255, 100, 70); margin: 0; padding: 0;\">Lunch<\/td><\/em><\/td>\n<\/tr>\n<tr>\n    <td>12:20\u201313:20<\/td>\n    <td style=\"color:rgb(255, 100, 70); margin: 0; padding: 0;\">Lunch<\/td>\n    <td>13:15 &#8211; 14:00<\/td>\n    <td><em> What can we learn from and through machine learning if the physics of \n    many-body systems is behind it?<\/em><br> <span style=\"color: rgb(70, 100, \n    255)\">Miriam Klopotek, Stuttgart<\/span><\/td>\n<\/tr>\n<tr>\n    <td>13:20\u201314:05<\/td>\n    <td><em> Is knowledge forever? An astronomical perspective<\/em><br> <span \n    style=\"color: rgb(70, 100, 255)\">Dominik Els\u00e4sser, Dortmund<\/span><\/td>\n    <td>14:00 &#8211; 14:45<\/td>\n    <td><em> Stakes and understanding the decisions of artificial intelligent \n    systems <\/em><br> <span style=\"color: rgb(70, 100, 255)\">Eva Schmidt, \n    Dortmund<\/span><\/td>\n<\/tr>\n<tr>\n    <td>14:05\u201314:50<\/td>\n    <td><em> ML-driven knowledge gain in physics<\/em><br> <span style=\"color: rgb(70, \n    100, 255)\">Wolfgang Rhode, Dortmund<\/span><\/td>\n    <td>14:45 &#8211; 15:30<\/td>\n    <td><em> A hypothesis-centric perspective on machine learning in biomedicine <\/em> \n    <br><span style=\"color: rgb(70, 100, 255)\"> Axel Mosig, Bochum<\/span><\/td>\n<\/tr>\n<tr>\n    <td>14:50\u201315:35<\/td>\n    <td><em>Data, theories, and machine learning in astroparticle physics <\/em><br> \n    <span style=\"color: rgb(70, 100, 255)\">Brigitte Falkenburg, Dortmund <\/span><\/td>\n    <td>15:30 &#8211; 15:45<\/td>\n    <td style=\"color:rgb(255, 100, 70); margin: 0; padding: 0;\">Coffee<\/td>\n<\/tr>\n<tr>\n    <td>15:35\u201315:50<\/td>\n    <td style=\"color:rgb(255, 100, 70); margin: 0; padding: 0;\">Coffee<\/td>\n    <td>15:45 &#8211; 16:30<\/td>\n    <td><em> (to be announced) <\/em><br> <span style=\"color: rgb(70, 100, \n    255)\">Kathleen Creel, Boston<\/span><\/td>\n<\/tr>\n<tr>\n    <td>15:50\u201316:35<\/td>\n    <td><em> Opacity as a stepping stone <\/em><br> <span style=\"color: rgb(70, 100, \n    255)\">Lena K\u00e4stner, Bayreuth<\/span><\/td>\n    <td>16:30 &#8211; 17:15<\/td>\n    <td><em>Deep learning for scientific discovery and the theory-freedom-robustness \n    trade-off<\/em><br><span style=\"color: rgb(70, 100, 255)\">Florian Boge, Michael \n    Kr\u00e4mer, Christian Zeitnitz, Dortmund \/ Aachen \/ Wuppertal<\/span> <\/td>\n<\/tr>\n<tr>\n    <td>16:35-17:20<\/td>\n    <td><em>Explainable machine learning in drug discovery<\/em><br> <span style=\"color: \n    rgb(70, 100, 255)\">J\u00fcrgen Bajorath, Bonn<\/span><\/td>\n    <td>17:15 &#8211; 17:20<\/td>\n    <td>Closing Words (FJB)<\/td>\n<\/tr>\n<tr>\n    <td>18:30 &#8211; 19:15<\/td>\n    <td style=\"color:rgb(255, 100, 70); margin: 0; padding: 0;\">Dortmunder U<\/td>\n    <td><br><\/td>\n    <td><br><\/td>\n<\/tr>\n<tr>\n   <td>20:00 &#8211;<\/td>\n   <td style=\"color:rgb(255, 100, 70); margin: 0; padding: 0;\">Dinner<\/td>\n   <td><br><\/td>\n   <td><br><\/td>\n<\/tr>\n\n\n<\/table>\n<\/h1>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-28f84493 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column has-small-font-size is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:500px\">\n<p><strong>Main Organizers<\/strong><\/p>\n<\/div>\n<\/div>\n\n\n\n<p>Annika Schuster, Frauke Stoll, and Florian J. Boge<\/p>\n\n\n\n<p><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Workshop: Epistemological Issues of Machine Learning in Science Date: 27.\u201328.02.2024 Place: Chaudoire Pavillon, TU Dortmund University Conference booklet Poster Description With impressive advances in Machine Learning (ML) and particularly Deep Learning, Artificial Intelligence is currently taking science by storm. This workshop brings together top scientists and philosophers working on fundamental issues connected to the use [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":163,"parent":129,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"header-footer-only","meta":{"footnotes":""},"class_list":["post-359","page","type-page","status-publish","has-post-thumbnail","hentry"],"_links":{"self":[{"href":"https:\/\/udnn.tu-dortmund.de\/index.php\/wp-json\/wp\/v2\/pages\/359","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/udnn.tu-dortmund.de\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/udnn.tu-dortmund.de\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/udnn.tu-dortmund.de\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/udnn.tu-dortmund.de\/index.php\/wp-json\/wp\/v2\/comments?post=359"}],"version-history":[{"count":32,"href":"https:\/\/udnn.tu-dortmund.de\/index.php\/wp-json\/wp\/v2\/pages\/359\/revisions"}],"predecessor-version":[{"id":964,"href":"https:\/\/udnn.tu-dortmund.de\/index.php\/wp-json\/wp\/v2\/pages\/359\/revisions\/964"}],"up":[{"embeddable":true,"href":"https:\/\/udnn.tu-dortmund.de\/index.php\/wp-json\/wp\/v2\/pages\/129"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/udnn.tu-dortmund.de\/index.php\/wp-json\/wp\/v2\/media\/163"}],"wp:attachment":[{"href":"https:\/\/udnn.tu-dortmund.de\/index.php\/wp-json\/wp\/v2\/media?parent=359"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}