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
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