Leakage and the reproducibility crisis in machine-learning-based science
Sayash Kapoor; Arvind Narayanan · 2023 · Patterns
WASTE classifies this as Replication Failure · AI classification, approximate
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Abstract
Machine-learning (ML) methods have gained prominence in the quantitative sciences. However, there are many known methodological pitfalls, including data leakage, in ML-based science. We systematically investigate reproducibility issues in ML-based science. Through a survey of literature in fields that have adopted ML methods, we find 17 fields where leakage has been found, collectively affecting 294 papers and, in some cases, leading to wildly overoptimistic conclusions. Based on our survey, we introduce a detailed taxonomy of eight types of leakage, ranging from textbook errors to open resear
Abstract by Sayash Kapoor; Arvind Narayanan, Patterns (2023) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1016/j.patter.2023.100804