Beyond Nutrition Labels: How Analogical Reasoning Shapes Synthetic Media Disclosure Design

TL;DR

This paper explores how AI policymakers design synthetic media disclosures, emphasizing the role of analogical reasoning in balancing transparency, harm reduction, and technical constraints amidst complex sociotechnical challenges.

Contribution

It provides an empirical analysis of decision-making processes in synthetic media disclosure design, highlighting the use of analogical reasoning to navigate tensions and inform policy strategies.

Findings

Identifies key disclosure goals like transparency and harm reduction.

Reveals tensions between normativity and neutrality, proactivity and precision.

Shows analogical reasoning from nutrition labels influences disclosure design.

Abstract

As synthetic media proliferates, AI policymakers and practitioners have increasingly turned to disclosures--signals describing how media has been created or modified by AI--to help audiences evaluate media credibility. While there is a growing body of research on user interpretations, the upstream decision-making processes that affect users remain underexplored. This study therefore examines how AI policymakers and practitioners design synthetic media disclosures under complex sociotechnical constraints. Drawing on 23 expert interviews and 13 case studies from organizations participating in the Partnership on AI's Synthetic Media Framework, analysis identifies key disclosure goals, including process transparency and harm reduction, and two central tensions that emerge when pursuing those goals: normativity versus neutrality and proactivity versus precision. Findings highlight the role of analogical reasoning, from nutrition labels to Prop 65 warnings, in managing, but not resolving tensions. Ultimately, this study emphasizes the need for scholarship focused on AI transparency decision-makers and their use of analogical reasoning to support audiences encountering media in the AI age.