mlsanityData to 7 Oct 2026

"We are not Future-ready": Understanding AI Privacy Risks and Existing Mitigation Strategies from the Perspective of AI Developers in Europe

arXiv 2510.00909 ↗First posted 1 Oct 2025cs.HC cs.AIcited by 2

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Abstract (arXiv)

The proliferation of AI has sparked privacy concerns related to training data, model interfaces, downstream applications, and more. We interviewed 25 AI developers based in Europe to understand which privacy threats they believe pose the greatest risk to users, developers, and businesses and what protective strategies, if any, would help to mitigate them. We find that there is little consensus among AI developers on the relative ranking of privacy risks. These differences stem from salient reasoning patterns that often relate to human rather than purely technical factors. Furthermore, while AI developers are aware of proposed mitigation strategies for addressing these risks, they reported minimal real-world adoption. Our findings highlight both gaps and opportunities for empowering AI developers to better address privacy risks in AI.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 16 of its 39 references that are in the map. First posted on arXiv 1 Oct 2025.

Builds on

16 of 39 references.

References · 16

  1. 2020Extracting Training Data from Large Language Modelscited by 2,144
  2. 2018The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networkscited by 847
  3. 2022Knowledge Unlearning for Mitigating Privacy Risks in Language Modelscited by 424
  4. 2017Wild Patterns: Ten Years After the Rise of Adversarial Machine Learningcited by 411
  5. 2023A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Uglycited by 380
  6. 2020A Survey of Privacy Attacks in Machine Learningcited by 143
  7. 2024On Protecting the Data Privacy of Large Language Models (LLMs): A Surveycited by 91
  8. 2022Are Attribute Inference Attacks Just Imputation?cited by 48
  9. 2017Guidelines for including grey literature and conducting multivocal literature reviews in software engineeringcited by 38
  10. 2023Deepfakes, Phrenology, Surveillance, and More! A Taxonomy of AI Privacy Riskscited by 31
  11. 2023Identifying and Mitigating Privacy Risks Stemming from Language Models: A Surveycited by 30
  12. 2023ChatGPT Needs SPADE (Sustainability, PrivAcy, Digital divide, and Ethics) Evaluation: A Reviewcited by 28
4 more
  1. 2024Privacy Risks of General-Purpose AI Systems: A Foundation for Investigating Practitioner Perspectivescited by 5
  2. 2024Evaluating Privacy Perceptions, Experience, and Behavior of Software Development Teamscited by 4
  3. 2024Generative AI Models: Opportunities and Risks for Industry and Authoritiescited by 3
  4. 2022Lessons Learned: Surveying the Practicality of Differential Privacy in the Industrycited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026SoK: Privacy Risks and Mitigations in Retrieval-Augmented Generation Systemscited by 7
  2. 2026Focused on the User, Overlooking the Risks: Security and Privacy Understandings, Practices and Challenges of Independent Chinese AI Agent Developerscited by 0