帮助AI产品设计者识别并应对隐私风险的工具
Privy: Envisioning and Mitigating Privacy Risks for Consumer-facing AI Product Concepts
- 通过结构化评估引导非隐私专家识别风险
- 实测显示评估质量获13位专家认可，大模型版效果更优
- 克服认知、动机、能力三类隐私防护障碍，适合产品团队使用
AI在创造和加剧隐私风险的同时，从业者却缺乏有效的识别与缓解资源。我们提出Privy，一种引导无隐私背景的实践者进行结构化隐私影响评估的工具，旨在：(i)识别新型AI产品概念中的相关风险，(ii)提出适当的缓解策略。Privy基于对11位从业者的形成性研究，发展出两个版本——一个由大语言模型驱动，另一个为模板式。我们通过24名独立实践者的对照实验评估这两个版本，其评估结果由13位独立隐私专家评审。结果显示，Privy帮助实践者生成了专家评定为高质量的隐私评估：能准确识别风险并提出合理缓解措施。大模型版本的效果进一步增强。实践者自身也评价Privy具有实用性和可用性，反馈表明该工具有效克服了隐私工作中长期存在的认知、动机与能力障碍。
原文摘要 · Abstract (English)
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.
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