Nissenbaum paper takes on privacy nihilism as AI inference erodes data-category frameworks
AllThingsApx · x · 2026-09-28
A new arXiv paper, Countering Privacy Nihilism by Severin Engelmann and Helen Nissenbaum, argues that accepting the premise that AI can infer 'everything from everything' would undermine privacy regulation built on data categories like sensitive/non-sensitive and private/public — a stance the authors call privacy nihilism. They introduce 'conceptual overfitting' to expose flawed epistemic practices behind hyperbolic AI capability claims, and propose moving to multi-parameter frameworks like contextual integrity that weigh the recipient's role and intended use of information.
More from Safety
- If a frontier lab admits its AI can't be contained, that lab should be shut down — kevinnbass · 2026-09-28
- STOC asks for AI-use disclosure but says it won't affect review — researchers ask what's the point — fortnow · 2026-09-28
- OpenAI slammed over security incidents: warned for months, still caught off guard — ShakeelHashim · 2026-09-28
- Roger Martin applies Buchanan's economics to AI regulatory strategy before enacting rules — RogerLMartin · 2026-09-28
- Steven Pinker backs Suleyman's case against AI rights and 'model welfare' — sapinker · 2026-09-28
- Jury Form in New Mexico v. Meta Lawsuit Now Publicly Available — hoofnagle · 2026-09-28