🤖 AI Summary
A concise guide from EFF clarifies confusing terms now driving new age‑gating laws: age gating (a policy that restricts access by age), age assurance (an umbrella term for any method used to assess age), age estimation (algorithmic guesses from photos, videos, voice, behavior or profile data), and age verification (requiring proof of identity like government ID, credit cards, biometrics). The piece warns that these terms are often used interchangeably by lawmakers and platforms—even though they imply very different technical approaches and privacy tradeoffs.
Technically, estimation systems (facial-analysis selfies, voice or behavioral signals) are imprecise and skewed: they frequently misclassify teenagers—the key group these laws target—and show higher error rates for people of color, trans and nonbinary people, and people with disabilities. When estimation flags uncertainty, platforms often force full verification, which equates to identity collection and long‑term storage of sensitive data. That creates obvious risks: centralized ID databases tied to browsing habits are high‑value targets for breaches. The core implication: vague language like “age assurance” can mask mass surveillance and disproportionate harms; policymakers and platforms must distinguish methods, account for bias and error rates, and prioritize privacy-preserving alternatives to avoid turning age checks into wholesale identity collection.
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