What Security and Privacy Transparency Users Need from Consumer-Facing Generative AI

2026-04-19

AI Summary

Current generative AI systems lack trustworthy and effective transparency regarding safety and privacy, hindering user adoption and engagement. This study addresses this gap by conducting semi-structured interviews and co-design workshops with 21 U.S. participants, followed by thematic analysis. Findings reveal that users rely on indirect signals to assess risk and that existing disclosures inadequately support initial decision-making. The research identifies five key dimensions of transparency and proposes an actionable framework centered on “independent assessment” and “on-demand disclosure.” This framework aims to empower users by enabling informed decisions and fostering trust, thereby advancing generative AI toward transparent practices that are both credible and usable.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Machine Learning: Transparent, Interpretable, Explainable ML

Application Category

• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Users increasingly rely on consumer-facing generative AI (GenAI) for tasks ranging from everyday needs to sensitive use cases. Yet, it remains unclear whether and how existing security and privacy (S&P) communications in GenAI tools shape users' adoption decisions and subsequent experiences. Understanding how users seek, interpret, and evaluate S&P information is critical for designing usable transparency that users can trust and act on. We conducted semi-structured interviews and design sessions with 21 U.S. GenAI users. We find that available S&P information rarely drove initial adoption in practice, as participants often perceived it as incomplete, ineffective, or lacking credibility. Instead, they relied on rough proxies, such as popularity, to infer S&P practices. After adoption, uncertainty about S&P practices constrained participants' willingness to use GenAI tools, particularly in high-stakes contexts, and, in some cases, contributed to discontinued use. Participants therefore called for transparency that supports decision-making and use, including trustworthy information (e.g., independent evaluations) and usable interfaces (e.g., on-demand disclosure). We synthesize participants' desired design practices into five dimensions to facilitate systematic future investigation into best practices. We conclude with recommendations for researchers, designers, and policymakers to improve S&P transparency in consumer-facing GenAI.

Problem

• security and privacy transparency
• generative AI
• user trust
• consumer-facing AI
• usable transparency

Innovation

• generative AI
• security and privacy transparency
• user-centered design
• trustworthy AI
• on-demand disclosure