Privy: Envisioning and Mitigating Privacy Risks for Consumer-facing AI Product Concepts
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911394968895488 |
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| author | Lee, Hao-Ping Yang, Yu-Ju Bilik, Matthew Krsek, Isadora von Davier, Thomas Serban Monteiro, Kyzyl Lin, Jason Agarwal, Shivani Forlizzi, Jodi Das, Sauvik |
| author_facet | Lee, Hao-Ping Yang, Yu-Ju Bilik, Matthew Krsek, Isadora von Davier, Thomas Serban Monteiro, Kyzyl Lin, Jason Agarwal, Shivani Forlizzi, Jodi Das, Sauvik |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_23525 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Privy: Envisioning and Mitigating Privacy Risks for Consumer-facing AI Product Concepts Lee, Hao-Ping Yang, Yu-Ju Bilik, Matthew Krsek, Isadora von Davier, Thomas Serban Monteiro, Kyzyl Lin, Jason Agarwal, Shivani Forlizzi, Jodi Das, Sauvik Human-Computer Interaction Artificial Intelligence 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. |
| title | Privy: Envisioning and Mitigating Privacy Risks for Consumer-facing AI Product Concepts |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2509.23525 |