Anti Patterns of Scientific Machine Learning to Fool the Masses:A Call for Open Science
Opening Keynote, PASC23, Monday, June 26, 2023. Davos, Switzerland.
Video on YouTube.
An anti-pattern is a frequently occurring pattern that is ineffective and risks being counterproductive. The term comes from software engineering, inspired by the classic book “Design Patterns” (highlighting desirable and effective patterns for code). Over the years, the term has spread beyond software 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 will call attention to anti-patterns in scientific machine learning—faintly tongue-in-cheek—with a call to do better. Scientific machine learning promises to help solve problems of high consequence in science, facing challenges like expensive or sparse data, complex scenarios, stringent accuracy requirements. It is expected to be domain-aware, interpretable, and robust. But realizing the potential is obstructed by anti-patterns: performance claims out of context, renaming old things, incomplete reporting, poor transparency, glossing over limitations, closet failures, overgeneralization, data negligence, gatekeeping, and puffery. Open science—the culture and practices that lead to a transparent scientific process and elevate collaboration—is the lens through which we can see a path for improvement. In the Year of Open Science, this talk is a call for a better way of doing and communicating science.