Claire Leibowicz

Claire R. Leibowicz, Christian H. Cardona

2023 was the year the world woke up to generative AI, and 2024 is the year policymakers are responding more firmly. Importantly, this policy momentum is taking place alongside real world creation and distribution of synthetic media. Social media platforms, news organizations, dating apps, image generation companies, and more are already navigating a world of AI-generated visuals and sounds, already changing hearts and minds, as policymakers try to catch up. How, then, can AI governance capture the complexity of the synthetic media landscape? How can it attend to synthetic media's myriad uses, ranging from storytelling to privacy preservation, to deception, fraud, and defamation, taking into account the many stakeholders involved in its development, creation, and distribution? And what might it mean to govern synthetic media in a manner that upholds the truth while bolstering freedom of expression? What follows is the first known collection of diverse examples of the implementation of synthetic media governance that responds to these questions, specifically through Partnership on AI's (PAI) Responsible Practices for Synthetic Media - a voluntary, normative Framework for creating, distributing, and building technology for synthetic media responsibly, launched in February 2023. In this paper, we present a case bank of real world examples that help operationalize the Framework - highlighting areas synthetic media governance can be applied, augmented, expanded, and refined for use, in practice. Read together, the cases emphasize distinct elements of AI policymaking and seven emergent best practices supporting transparency, safety, expression, and digital dignity online: consent, disclosure, and differentiation between harmful and creative use cases.

Claire Leibowicz, Sean McGregor, Aviv Ovadya

Synthetic media detection technologies label media as either synthetic or non-synthetic and are increasingly used by journalists, web platforms, and the general public to identify misinformation and other forms of problematic content. As both well-resourced organizations and the non-technical general public generate more sophisticated synthetic media, the capacity for purveyors of problematic content to adapt induces a \newterm{detection dilemma}: as detection practices become more accessible, they become more easily circumvented. This paper describes how a multistakeholder cohort from academia, technology platforms, media entities, and civil society organizations active in synthetic media detection and its socio-technical implications evaluates the detection dilemma. Specifically, we offer an assessment of detection contexts and adversary capacities sourced from the broader, global AI and media integrity community concerned with mitigating the spread of harmful synthetic media. A collection of personas illustrates the intersection between unsophisticated and highly-resourced sponsors of misinformation in the context of their technical capacities. This work concludes that there is no "best" approach to navigating the detector dilemma, but derives a set of implications from multistakeholder input to better inform detection process decisions and policies, in practice.

Emily Saltz, Claire Leibowicz, Claire Wardle

Since 2016, the amount of academic research with the keyword "misinformation" has more than doubled [2]. This research often focuses on article headlines shown in artificial testing environments, yet misinformation largely spreads through images and video posts shared in highly-personalized platform contexts. A foundation of qualitative research is necessary to begin filling this gap to ensure platforms' visual misinformation interventions are aligned with users' needs and understanding of information in their personal contexts, across platforms. In two studies, we combined in-depth interviews (n=15) with diary and co-design methods (n=23) to investigate how a broad mix of Americans exposed to misinformation during COVID-19 understand their visual information environments, including encounters with interventions such as Facebook fact-checking labels. Analysis reveals a deep division in user attitudes about platform labeling interventions for visual information which are perceived by many as overly paternalistic, biased, and punitive. Alongside these findings, we discuss our methods as a model for continued independent qualitative research on cross-platform user experiences of misinformation that inform interventions.