Neat vs. Scruffy: How Early AI Researchers Classified Epistemic Cultures of Knowledge Representation

Authors

• Lindsay Poirier, Smith CollegeFollow

Author ORCID Identifier

Lindsay Poirier: 0000-0001-9307-5834

Document Type

Article

Publication Date

6-10-2025

Publication Title

IEEE Annals of the History of Computing

Abstract

Accounts of the history of symbolic artificial intelligence (AI) often categorize researchers and their programs through binary oppositions: logicist versus procedural, or alternatively, neat versus scruffy. These terms were often referenced throughout the 1970s and 1980s by AI researchers at institutions in the United States and Europe to document epistemic and esthetic differences in approaches to knowledge representation. In this article, I historicize and situate these binary oppositions, showing how researchers leveraged them in discourse to demarcate the beliefs and commitments underpinning certain AI approaches. I argue that the labels helped knowledge representation scholars identify, trouble, and grapple with the epistemological and stylistic pluralism of the field, resituate epistemic legitimacy and authority with certain design approaches, and in turn constitute the boundaries of symbolic AI.

Recommended Citation

Poirier, Lindsay, "Neat vs. Scruffy: How Early AI Researchers Classified Epistemic Cultures of Knowledge Representation" (2025). Statistical and Data Sciences: Faculty Publications, Smith College, Northampton, MA.
https://scholarworks.smith.edu/sds_facpubs/90

Rights

© 2024 The Authors.

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Version of Record

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.