mlsanityData to 14 Oct 2026

Privy: Envisioning and Mitigating Privacy Risks for Consumer-facing AI Product Concepts

arXiv 2509.23525 ↗First posted 27 Sep 2025cs.HC cs.AIcited by 4

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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

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  3. 2023Generative artificial intelligence enhances creativity but reduces the diversity of novel contentcited by 167
  4. 2023From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacycited by 143
  5. 2017Towards a Visual Privacy Advisor: Understanding and Predicting Privacy Risks in Imagescited by 101
  6. 2022Dislocated Accountabilities in the AI Supply Chain: Modularity and Developers' Notions of Responsibilitycited by 33
  7. 2023Deepfakes, Phrenology, Surveillance, and More! A Taxonomy of AI Privacy Riskscited by 31
  8. 2023How Knowledge Workers Think Generative AI Will (Not) Transform Their Industriescited by 30
  9. 2024Farsight: Fostering Responsible AI Awareness During AI Application Prototypingcited by 28
  10. 2023AHA!: Facilitating AI Impact Assessment by Generating Examples of Harmscited by 26
  11. 2023Walking the Walk of AI Ethics: Organizational Challenges and the Individualization of Risk among Ethics Entrepreneurscited by 12
  12. 2024ExploreGen: Large Language Models for Envisioning the Uses and Risks of AI Technologiescited by 12
10 more
  1. 2024AI Should Challenge, Not Obeycited by 11
  2. 2024Co-designing an AI Impact Assessment Report Template with AI Practitioners and AI Compliance Expertscited by 10
  3. 2023Ethics in the Age of AI: An Analysis of AI Practitioners' Awareness and Challengescited by 6
  4. 2024Antagonistic AIcited by 6
  5. 2025The "Who", "What", and "How" of Responsible AI Governance: A Systematic Review and Meta-Analysis of (Actor, Stage)-Specific Toolscited by 6
  6. 2024Privacy Risks of General-Purpose AI Systems: A Foundation for Investigating Practitioner Perspectivescited by 5
  7. 2024Evaluating Privacy Perceptions, Experience, and Behavior of Software Development Teamscited by 4
  8. 2024Supporting Industry Computing Researchers in Assessing, Articulating, and Addressing the Potential Negative Societal Impact of Their Workcited by 4
  9. 2020Privacy Engineering Meets Software Engineering. On the Challenges of Engineering Privacy ByDesigncited by 3
  10. 2016Ontologies for Privacy Requirements Engineering: A Systematic Literature Reviewcited by 1

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Citing papers per year: 2026: 4.

4 papers.