The worst of both worlds: A comparative analysis of errors in learning from data in psychology and machine learning

• URL: http://arxiv.org/abs/2203.06498v2
• Date: Wed, 16 Mar 2022 14:47:19 GMT
• Title: The worst of both worlds: A comparative analysis of errors in learning from data in psychology and machine learning
• Authors: Jessica Hullman, Sayash Kapoor, Priyanka Nanayakkara, Andrew Gelman, and Arvind Narayanan
• Abstract summary: Recent concerns that machine learning (ML) may be facing a misdiagnosis and replication crisis suggest that some published claims in ML research cannot be taken at face value. A deeper understanding of what concerns in research in supervised ML have in common with the replication crisis in experimental science can put the new concerns in perspective.
• Score: 17.336655978572583
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent concerns that machine learning (ML) may be facing a reproducibility and replication crisis suggest that some published claims in ML research cannot be taken at face value. These concerns inspire analogies to the replication crisis affecting the social and medical sciences, as well as calls for greater integration of statistical approaches to causal inference and predictive modeling. A deeper understanding of what reproducibility concerns in research in supervised ML have in common with the replication crisis in experimental science can put the new concerns in perspective, and help researchers avoid "the worst of both worlds" that can emerge when ML researchers begin borrowing methodologies from explanatory modeling without understanding their limitations, and vice versa. We contribute a comparative analysis of concerns about inductive learning that arise in different stages of the modeling pipeline in causal attribution as exemplified in psychology versus predictive modeling as exemplified by ML. We identify themes that re-occur in reform discussions like overreliance on asymptotic theory and non-credible beliefs about real-world data generating processes. We argue that in both fields, claims from learning are implied to generalize outside the specific environment studied (e.g., the input dataset or subject sample, modeling implementation, etc.) but are often impossible to refute due to forms of underspecification. In particular, many errors being acknowledged in ML expose cracks in long-held beliefs that optimizing predictive accuracy using huge datasets absolves one from having to make assumptions about the underlying data generating process. We conclude by discussing rhetorical risks like error misdiagnosis that arise in times of methodological uncertainty.

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