Redefining “Open” in Platform- and AI-Mediated Innovation
From Artifact Disclosure to Infrastructural Openness
DOI:
https://doi.org/10.51094/jxiv.5266キーワード:
open source、 open-source AI、 reproducibility、 openwashing、 data governance抄録
The vocabulary of “openness” that organizes innovation strategy was developed for a world in which the central object of disclosure was source code released once under a license. This paper argues that this artifact-centric conception is increasingly mismatched with platform- and AI-mediated innovation, where value resides in data, trained models, and distribution channels rather than code, and where disclosure no longer guarantees that anyone can verify, govern, or act upon what is disclosed. Using a problematization approach (Alvesson & Sandberg, 2011), the paper surfaces three assumptions in established openness theory—that the disclosed artifact carries the value, that boundaries are stable, and that disclosure constrains behavior symmetrically—and shows, through documented episodes of relicensing, “fauxpen” licensing, and contested “open” AI, how each fails. It then reconceptualizes openness as an infrastructural rather than documentary property, specifying three conditions under which disclosure yields openness: verifiable transparency, open data and model governance, and distributed openness. Derived from the failed assumptions, these operate at the level of ecosystem infrastructure beneath the transactions catalogued by Dahlander and Gann (2010). The paper develops three propositions, connects them to debates over the Open Source AI Definition and the EU AI Act, and outlines an empirical agenda.
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投稿日時: 2026-06-27 07:41:43 UTC
公開日時: 2026-07-09 02:24:49 UTC
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