Message posted on 15/07/2020
CfP: Minds & Machines Special Issue on "Machine Learning: Prediction Without Explanation?"
Call for Papers for a /Minds & Machines/ Special Issue on <br> <br>*Machine Learning: Prediction Without Explanation? <br>*https://www.springer.com/journal/11023/updates/18180316 <br> <br>_Description _ <br>Over the last decades, Machine Learning (ML) techniques have gained <br>central prominence in many areas of science. ML typically aims at <br>pattern recognition and prediction, and in many cases has become a <br>better tool for these purposes than traditional methods. The downside, <br>however, is that ML does not seem to provide any explanations, at least <br>not in the same sense as theories or traditional models do. <br> <br>This apparent lack of explanation is often also linked to the opacity of <br>ML techniques, sometimes referred to as the ‘Black Box Challenge’. <br>Methods such as heat maps or adversarial examples are aimed at reducing <br>this opacity and opening the black box. But at present, it remains an <br>open question how and what exactly these methods explain and what the <br>nature of these explanations is. <br>While in some areas of science this may not create any interesting <br>philosophical challenges, in many fields, such as medicine, climate <br>science, or particle physics, an explanation may be desired; among other <br>things for the sake of rendering subsequent decisions and policy making <br>transparent. Moreover, explanation and understanding are traditionally <br>construed as central epistemic aims of science in general. Does a turn <br>to ML techniques hence imply a radical shift in the aims of science? <br>Does it require us to rethink science-based policy making? Or does it <br>mean we need to rethink our concepts of explanation and understanding? <br> <br>In this Special Issue, we want to address this complex of questions <br>regarding explanation and prediction, as it attaches to ML applications <br>in science and beyond. <br>We invite papers focusing on but not restricted to the following topics: <br> <br>• (How) can ML results be used for the sake of explaining scientific <br>observations? <br>• If so, what is the nature of these explanations? <br>• Will future science favor prediction above explanation? <br>• If so, what does this mean for science-based decision and policy <br>making? <br>• What is explained about ML by methods such as saliency maps and <br>adversarials? <br>• Does ML introduce a shift from classical notions of scientific <br>explanation, such as causal-mechanistic, covering law-, or <br>unification-based, towards a purely statistical one? <br>• (Why) should we trust ML applications, given their opacity? <br>• (Why) should we care about the apparent loss of explanatory power? <br> <br>The Special Issue is guest edited by members of the project /The impact <br>of computer simulations and machine learning on the epistemic status of <br>LHC Data/, part of the DFG/FWF-funded interdisciplinary research unit <br>/The Epistemology of the Large Hadron Collider <br> <br>/ For more information, please visit <br>https://www.lhc-epistemology.uni-wuppertal.de <br>_ <br>Timetable _ <br>Deadline for paper submissions: 28 February 2021 <br>Deadline for paper reviewing: 19 April 2021 <br>Deadline for submission of revised papers: 03 May 2021 <br>Deadline for reviewing revised papers: 07 June 2021 <br>Papers will be published in 2021 <br> <br>_Submission Details_ <br>To submit a paper for this special issue, authors should go to the <br>journal’s Editorial Manager <br>https://www.editorialmanager.com/mind/default.aspx The author (or a <br>corresponding author for each submission in case of co- authored papers) <br>must register into EM. <br>The author must then select the special article type: "Machine Learning: <br>Prediction without Explanation?” from the selection provided in the <br>submission process. This is needed in order to assign the submissions to <br>the Guest Editor. <br>Submissions will then be assessed according to the following procedure: <br>New Submission => Journal Editorial Office => Guest Editor(s) => <br>Reviewers => Reviewers’ Recommendations => Guest Editor(s)’ <br>Recommendation => Editor-in-Chief’s Final Decision => Author <br>Notification of the Decision. <br>The process will be reiterated in case of requests for revisions. <br> <br>_Guest Editors__ <br>_ • Dr. Florian J. Boge, postdoctoral researcher, Interdisciplinary <br>Centre for Science and Technology Studies (IZWT), Wuppertal University <br>• Paul Grünke, doctoral student, research group “Philosophy of <br>Engineering, Technology Assessment, and Science”, Institute for <br>Technology Assessment and Systems Analysis (ITAS), Karlsruhe Institute <br>of Technology (KIT) <br>• Prof. Dr. Dr. Rafaela Hillerbrand, head of the research group <br>“Philosophy of Engineering, Technology Assessment, and Science”, <br>Institute for Technology Assessment and Systems Analysis (ITAS), <br>Karlsruhe Institute of Technology (KIT) <br> <br>For any further information please contact: <br>- Dr. Florian J. Boge: fjboge@uni-wuppertal.de <br>- Paul Grünke: paul.gruenke@kit.edu <br>_______________________________________________ <br>EASST's Eurograd mailing list <br>Eurograd (at) lists.easst.net <br>Unsubscribe or edit subscription options: http://lists.easst.net/listinfo.cgi/eurograd-easst.net <br> <br>Meet us via https://twitter.com/STSeasst <br> <br>Report abuses of this list to Eurograd-owner@lists.easst.netview formatted text
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