What Security and Privacy Transparency Users Need from Consumer-Facing Generative AI
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911605526102016 |
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| author | Cao, Jiaxun Dong, Yu Zhan, Chunxi Neti, Rithvik Peddinti, Sai Teja Emami-Naeini, Pardis |
| author_facet | Cao, Jiaxun Dong, Yu Zhan, Chunxi Neti, Rithvik Peddinti, Sai Teja Emami-Naeini, Pardis |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_17270 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | What Security and Privacy Transparency Users Need from Consumer-Facing Generative AI Cao, Jiaxun Dong, Yu Zhan, Chunxi Neti, Rithvik Peddinti, Sai Teja Emami-Naeini, Pardis Human-Computer Interaction Artificial Intelligence Cryptography and Security Computers and Society 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. |
| title | What Security and Privacy Transparency Users Need from Consumer-Facing Generative AI |
| topic | Human-Computer Interaction Artificial Intelligence Cryptography and Security Computers and Society |
| url | https://arxiv.org/abs/2604.17270 |