The Worst of Both Worlds: A Comparative Analysis of Errors in Learning from Data in Psychology and Machine Learning

Jessica Hullman, Sayash Kapoor, Priyanka Nanayakkara, Andrew Gelman, Arvind Narayanan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

Arguments that machine learning (ML) is facing a reproducibility and replication crisis suggest that some published claims in research cannot be taken at face value. Concerns inspire analogies to the replication crisis affecting the social and medical sciences. A deeper understanding of what reproducibility concerns in supervised ML research have in common with the replication crisis in experimental science puts the new concerns in perspective, and helps researchers avoid "the worst of both worlds,"where 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 causal attribution as exemplified in psychology versus predictive modeling as exemplified in ML. We identify common themes 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 difficult to refute due to underspecification of key parts of the learning pipeline. We conclude by discussing risks that arise when sources of errors are misdiagnosed and the need to acknowledge the role of human inductive biases in learning and reform.

Original languageEnglish (US)
Title of host publicationAIES 2022 - Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society
PublisherAssociation for Computing Machinery, Inc
Pages335-348
Number of pages14
ISBN (Electronic)9781450392471
DOIs
StatePublished - Jul 26 2022
Externally publishedYes
Event5th AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society, AIES 2022 - Oxford, United Kingdom
Duration: Aug 1 2022Aug 3 2022

Publication series

NameAIES 2022 - Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society

Conference

Conference5th AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society, AIES 2022
Country/TerritoryUnited Kingdom
CityOxford
Period8/1/228/3/22

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Social Sciences (miscellaneous)

Keywords

  • generalizability
  • machine learning
  • replication
  • science reform

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