Privy: Envisioning and Mitigating Privacy Risks for Consumer-facing AI Product Concepts
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Abstract (arXiv)
AI creates and exacerbates privacy risks, yet practitioners lack effective resources to identify and mitigate these risks. We present Privy, a tool that guides practitioners without privacy expertise through structured privacy impact assessments to: (i) identify relevant risks in novel AI product concepts, and (ii) propose appropriate mitigations. Privy was shaped by a formative study with 11 practitioners, which informed two versions -- one LLM-powered, the other template-based. We evaluated these two versions of Privy through a between-subjects, controlled study with 24 separate practitioners, whose assessments were reviewed by 13 independent privacy experts. Results show that Privy helps practitioners produce privacy assessments that experts deemed high quality: practitioners identified relevant risks and proposed appropriate mitigation strategies. These effects were augmented in the LLM-powered version. Practitioners themselves rated Privy as being useful and usable, and their feedback illustrates how it helps overcome long-standing awareness, motivation, and ability barriers in privacy work.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 22 of its 83 references that are in the map. First posted on arXiv 27 Sep 2025.
Builds on
22 of 83 references.
References · 22
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10 more
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Built on by
Citing papers per year: 2026: 4.
4 papers.
- 2026How Well Can LLM Agents Simulate End-User Security and Privacy Attitudes and Behaviors?cited by 3
- 2026Understanding U.S. Users' Security and Privacy Transparency Needs for Consumer-Facing Generative AIcited by 0
- 2026The Perils of Agency: How Developers Perceive, Prioritize, and Address Risks in Agentic AI Productscited by 0
- 2026A Human-LLM Teaming Framework for Privacy Risk Analysis: An Illustration with CBDC-Based Welfare Schemescited by 0
- 2026A Human-LLM Teaming Framework for Privacy Risk Analysis: An Illustration with CBDC-Based Welfare Schemes17 Aug 2026
- 2026The Perils of Agency: How Developers Perceive, Prioritize, and Address Risks in Agentic AI Products13 Jun 2026
- 2026Understanding U.S. Users' Security and Privacy Transparency Needs for Consumer-Facing Generative AI19 Apr 2026
- 2026How Well Can LLM Agents Simulate End-User Security and Privacy Attitudes and Behaviors?6 Feb 2026