mlsanityData to 14 Oct 2026

Deepfakes, Phrenology, Surveillance, and More! A Taxonomy of AI Privacy Risks

arXiv 2310.07879 ↗First posted 11 Oct 2023cs.HCcited by 31

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Abstract (arXiv)

Privacy is a key principle for developing ethical AI technologies, but how does including AI technologies in products and services change privacy risks? We constructed a taxonomy of AI privacy risks by analyzing 321 documented AI privacy incidents. We codified how the unique capabilities and requirements of AI technologies described in those incidents generated new privacy risks, exacerbated known ones, or otherwise did not meaningfully alter the risk. We present 12 high-level privacy risks that AI technologies either newly created (e.g., exposure risks from deepfake pornography) or exacerbated (e.g., surveillance risks from collecting training data). One upshot of our work is that incorporating AI technologies into a product can alter the privacy risks it entails. Yet, current approaches to privacy-preserving AI/ML (e.g., federated learning, differential privacy, checklists) only address a subset of the privacy risks arising from the capabilities and data requirements of AI.

Key facts. Cited by 31 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Oct 2023.

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Citing papers per year: 2024: 10; 2025: 9; 2026: 12.

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  1. 2024How Far Are We From AGI: Are LLMs All We Need?cited by 31
  2. 2024Granular Privacy Control for Geolocation with Vision Language Modelscited by 21
  3. 2024ValueCompass: A Framework for Measuring Contextual Value Alignment Between Human and LLMscited by 20
  4. 2024Not My Voice! A Taxonomy of Ethical and Safety Harms of Speech Generatorscited by 18
  5. 2025AI Chatbots for Mental Health: Values and Harms from Lived Experiences of Depressioncited by 13
  6. 2025TALES: A Taxonomy and Analysis of Cultural Representations in LLM-generated Storiescited by 10
  7. 2026MemPrivacy: Privacy-Preserving Personalized Memory Management for Edge-Cloud Agentscited by 7
  8. 2025Helping Johnny Make Sense of Privacy Policies with LLMscited by 5
  9. 2024Measuring, Modeling, and Helping People Account for Privacy Risks in Online Self-Disclosures with AIcited by 4
  10. 2025Privy: Envisioning and Mitigating Privacy Risks for Consumer-facing AI Product Conceptscited by 4
  11. 2024Sound Check: Auditing Audio Datasetscited by 3
  12. 2024Data Defenses Against Large Language Modelscited by 2
19 more
  1. 2025"We are not Future-ready": Understanding AI Privacy Risks and Existing Mitigation Strategies from the Perspective of AI Developers in Europecited by 2
  2. 2026Privacy in Human-AI Romantic Relationships: Concerns, Boundaries, and Agencycited by 2
  3. 2024Forensics Adapter: Unleashing CLIP for Generalizable Face Forgery Detectioncited by 1
  4. 2025"Impressively Scary:" Exploring User Perceptions and Reactions to Unraveling Machine Learning Models in Social Media Applicationscited by 1
  5. 2026Understanding Users' Privacy Reasoning and Behaviors During Chatbot Use to Support Meaningful Agency in Privacycited by 1
  6. 2026Do Vision-Language Models Respect Contextual Integrity in Location Disclosure?cited by 1
  7. 2026CAREBench: A Child-Safety Risk Benchmark for Language Modelscited by 1
  8. 2024Deep Learning Under Siege: Identifying Security Vulnerabilities and Risk Mitigation Strategiescited by 0
  9. 2024Responsible AI in Open Ecosystems: Reconciling Innovation with Risk Assessment and Disclosurecited by 0
  10. 2025Who is Responsible When AI Fails? Mapping Causes, Entities, and Consequences of AI Privacy and Ethical Incidentscited by 0
  11. 2025AI Security Map: Holistic Organization of AI Security Technologies and Impacts on Stakeholderscited by 0
  12. 2025A Taxonomy of Data Risks in AI and Quantum Computing (QAI) - A Systematic Reviewcited by 0
  13. 2026Humanizing AI Grading: Student-Centered Insights on Fairness, Trust, Consistency and Transparencycited by 0
  14. 2026Investigating In-Context Privacy Learning by Integrating User-Facing Privacy Tools into Conversational Agentscited by 0
  15. 2026Promoting Critical Thinking With Domain-Specific Generative AI Provocationscited by 0
  16. 2026Understanding U.S. Users' Security and Privacy Transparency Needs for Consumer-Facing Generative AIcited by 0
  17. 2026The Perils of Agency: How Developers Perceive, Prioritize, and Address Risks in Agentic AI Productscited by 0
  18. 2026Visible to the Court: How AI Is (and Isn't) Litigated in U.S. Federal Court Opinionscited by 0
  19. 2026"Nothing to See Here'': Unintended Disclosure through Revision Traces of LLM Deliverablescited by 0