Navigating Uncertainties: How GenAI Developers Document Their Models on Open-Source Platforms
Year: 2026
Type: conference-paper
Abstract: Model documentation plays a crucial role in promoting responsible AI (RAI) development. The emergence of Generative AI (GenAI) models has reshaped the conditions under which documentation is produced, particularly on open-source platforms where models are hosted and shared. To examine how these changes have manifested in developers’ documentation practices, we interviewed 17 GenAI developers who document models on open-source platforms. Our findings illustrate that uncertainties have become a defining feature of developers’ GenAI documentation practices and that these uncertainties unfold in three interrelated forms: (1) normative and epistemic uncertainties in determining documentation content; (2) methodological uncertainties in evaluating and communicating model properties; and (3) ecosystemic uncertainties about who should document. We argue that these uncertainties in GenAI documentation require coordinated interventions, including infrastructural support to address epistemic and methodological uncertainties, community-based mechanisms to cultivate RAI documentation norms, and collaboration across supply-chain actors to address ecosystemic uncertainties.
Authors: Ningjing Tang, Megan Li, Amy Winecoff, Michael Madaio, Hoda Heidari, Hong Shen
Institutions: Carnegie Mellon University, Google (United States)
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4417049777