"We are not Future-ready": Understanding AI Privacy Risks and Existing Mitigation...
summary
25 interviews across 11 European countries show AI developers recognize risk amplification from bigger models but disagree strongly on priorities—23 unique rankings from five risk categories. Common practices are data cleaning and anonymization; advanced PETs and differential privacy are known but rarely adopted, with DP called a gold standard yet seen as impractical. Findings highlight internal, experience-driven reasoning, low real-world mitigation uptake, and uncertainty about whether regulation can map onto such diverse developer perspectives.
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chapters (6)
- 0:00AI privacy risks study
- 1:13Research focus and methodology
- 3:40Developer perceptions of privacy risks
- 6:30Mitigation strategies awareness
- 8:37Future outlook and stakeholders
- 11:45Key takeaways and limitations
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