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DTSTAMP:20230831T095742Z
LOCATION:Davos
DTSTART;TZID=Europe/Stockholm:20230626T103000
DTEND;TZID=Europe/Stockholm:20230626T112000
UID:submissions.pasc-conference.org_PASC23_sess103_key101@linklings.com
SUMMARY:AK01 - Anti Patterns of Scientific Machine Learning to Fool the Ma
 sses: A Call for Open Science
DESCRIPTION:Keynote\n\nLorena Barba (George Washington University)\n\nAn a
 nti-pattern is a frequently occurring pattern that is ineffective and risk
 s being counterproductive. The term comes from software engineering, inspi
 red by the classic book “Design Patterns” (highlighting desirable and effe
 ctive patterns for code). Over the years, the term has spread beyond softw
 are to other fields, like project management. An anti-pattern is recurring
 , it has bad consequences, and a better solution exists. Documenting anti-
 patterns is effective in revealing how to make improvements. This talk wil
 l call attention to anti-patterns in scientific machine learning—faintly t
 ongue-in-cheek—with a call to do better. Scientific machine learning promi
 ses to help solve problems of high consequence in science, facing challeng
 es like expensive or sparse data, complex scenarios, stringent accuracy re
 quirements. It is expected to be domain-aware, interpretable, and robust. 
 But realizing the potential is obstructed by anti-patterns: performance cl
 aims out of context, renaming old things, incomplete reporting, poor trans
 parency, glossing over limitations, closet failures, overgeneralization, d
 ata negligence, gatekeeping, and puffery. Open science—the culture and pra
 ctices that lead to a transparent scientific process and elevate collabora
 tion—is the lens through which we can see a path for improvement. In the Y
 ear of Open Science, this talk is a call for a better way of doing and com
 municating science.\n\nSession Chair: Michael A. Heroux (Sandia National L
 aboratories, St. John's University)
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