Opening the Scope of Openness in AI

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Citation: Tamara Paris, AJung Moon, Jin L.C. Guo Opening the Scope of Openness in AI.
DOI (original publisher): 10.1145/3715275.3732087
Semantic Scholar (metadata): 10.1145/3715275.3732087
Sci-Hub (fulltext): 10.1145/3715275.3732087
Internet Archive Scholar (search for fulltext): Opening the Scope of Openness in AI
Wikidata (metadata): Q135643660
Download: https://dl.acm.org/doi/10.1145/3715275.3732087
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Summary

Authors surface 98 openness concepts (via topic modeling over a multidisciplinary corpus) and qualitatively group them into three themes and three approaches to defining openness, then discuss how these map to AI. The themes are:

  • Interactivity (sub-themes: access, inspectability, distribution, reuse, collaboration)
  • Freedom (no obstacle, organic, non-isolation, broader boundaries, undetermined, autonomy)
  • Inclusiveness (fairness, diversity, democratization)

The approaches are:

  • properties
  • afforded actions (what openness enables or prevents)
  • desired effects

Authors note that widely cited definitions and frameworks, e.g., the Open Source AI Definition (OSI) and the Model Openness Framework, primarily operationalize openness via afforded actions (who can access/use/study/modify/share, under what conditions, and for which components). They encourage future work on under-explored parts of the taxonomy (e.g., collaboration, non-isolation, diversity) and on making the taxonomy actionable for AI governance and practice.

Theoretical and Practical Relevance

The taxonomy gives a shared vocabulary to specify what is open, to whom, how, and to what end, helping compare openness claims (open weights/data/eval) and revealing gaps where policy or practice may need to balance openness with other objectives (e.g., safety, privacy, equity).

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