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Rigor in AI: Doing Rigorous AI Work Requires a Broader, Responsible AI-Informed Conception of Rigor
Conference proceeding   Open access

Rigor in AI: Doing Rigorous AI Work Requires a Broader, Responsible AI-Informed Conception of Rigor

Alexandra Olteanu, Su Lin Blodgett, Agathe Balayn, Angelina Wang, Fernando Diaz, Flavio du Pin Calmon, Margaret Mitchell, Michael Ekstrand, Reuben Binns and Solon Barocas
39th Conference on Neural Information Processing Systems, NeurIPS 2025, v 38, pp 20127-20147
2025
DOI: https://doi.org/10.52202/085713-0597
url
https://doi.org/10.52202/085713-0597View
Published, Version of Record (VoR) Open Open Access (License Unspecified)

Abstract

Artificial Intelligence or Cybernetics
In AI research and practice, rigor remains largely understood in terms of methodological rigor—such as whether mathematical, statistical, or computational methods are correctly applied. We argue that this narrow conception of rigor has contributed to the concerns raised by the responsible AI community, including overblown claims about the capabilities of AI systems. Our position is that a broader conception of what rigorous AI research and practice should entail is needed. We believe such a conception—in addition to a more expansive understanding of (1) methodological rigor—should include aspects related to (2) what background knowledge informs what to work on (epistemic rigor); (3) how disciplinary, community, or personal norms, standards, or beliefs influence the work (normative rigor); (4) how clearly articulated the theoretical constructs under use are (conceptual rigor); (5) what is reported and how (reporting rigor); and (6) how well-supported the inferences from existing evidence are (interpretative rigor). In doing so, we also provide useful language and a framework for much-needed dialogue about the AI community’s work byresearchers, policymakers, journalists, and other stakeholders.

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