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.

Websites implement cookie consent interfaces to obtain users’ permission to use non-essential cookies, as required by privacy regulations. We extend prior research evaluating the impact of interface design on cookie consent through an online behavioral experiment (n = 1359) in which we prompted mobile and desktop users from the UK and US to make cookie consent decisions using one of 14 interfaces implemented with the OneTrust consent management platform (CMP). We found significant effects on user behavior and sentiment for multiple explanatory variables, including more negative sentiment towards the consent process among UK participants and lower comprehension of interface information among mobile users. The design factor that had the largest effect on user behavior was the initial set of options displayed in the cookie banner. In addition to providing more evidence of the inadequacy of current cookie consent processes, our results have implications for website operators and CMPs.

Many websites have added cookie consent interfaces to meet regulatory consent requirements. While prior work has demonstrated that they often use dark patterns — design techniques that lead users to less privacy-protective options — other usability aspects of these interfaces have been less explored. This study contributes a comprehensive, two-stage usability assessment of cookie consent interfaces. We first inspected 191 consent interfaces against five dark pattern heuristics and identified design choices that may impact usability. We then conducted a 1,109-participant online between-subjects experiment exploring the usability impact of seven design parameters. Participants were exposed to one of 12 consent interface variants during a shopping task on a prototype e-commerce website and answered a survey about their experience. Our findings suggest that a fully-blocking consent interface with in-line cookie options accompanied by a persistent button enabling users to later change their consent decision best meets several design objectives.