A new bill in New York would require disclaimers on AI-generated news content

Original Article ↗ Hacker News Discussion ↗

Inevitability of AI vs. Role of Regulation

  • Some argue resistance to AI (disclaimers, bans) is emotional “status quo bias”; once a technology spreads, it can be regulated but not rolled back.
  • Others reject this fatalism, pointing out past social reforms (unions, rights, etc.) and insisting society can still shape AI’s use, especially in news.

Why Label AI-Generated News at All?

  • Concerns: AI news is often regurgitated, low‑value, and easy to weaponize for propaganda, fake reviews, political messaging, or deceptive ads.
  • News, in particular, should minimize “hallucinations” because misinformation cascades.
  • Some want all AI-generated content labeled, not just news; a few would prefer AI content banned entirely.
  • Others emphasize accountability: human editors and publishers should remain fully responsible for AI-assisted output.

Prop 65 Analogy and Overlabeling

  • Many predict a “California cancer warning” outcome: everything gets labeled “may contain AI,” users tune it out, and the signal becomes useless.
  • Overcompliance is expected because proving “no AI was used” is hard; risk‑averse organizations may label everything.
  • Counterarguments note Prop 65 did push companies away from toxic chemicals; labels can still shift behavior even if ubiquitous.

Enforcement, Detectability, and Abuse Risks

  • Technical detection of AI text is seen as inherently unreliable, especially as models improve and can mimic “human sloppiness.”
  • That implies laws will mostly bind honest actors; bad actors and foreign propagandists will ignore them.
  • Some fear selective or partisan enforcement (e.g., targeting disfavored outlets) and new litigation/trolling niches.
  • Others stress that many regulations (food safety, emissions, etc.) work via process audits and whistleblowers, not perfect detection.

Definitions, Edge Cases, and Scope

  • Major ambiguity: what counts as “substantially composed” by AI vs. AI-assisted (spellcheck, Photoshop, search, classifiers, summarizers)?
  • Worries that everything from camera filters to light AI editing will trigger labels, making them meaningless.
  • Some suggest tiered labels (AI-assisted vs AI-generated) or standards work (e.g., W3C disclosure schema).
  • There are First Amendment concerns about compelled speech; commercial vs. noncommercial content distinctions are debated.

Alternatives and Complements

  • Proposals include:
    • Labels for original reporting and explicit sourcing, independent of AI use.
    • Strong liability for misleading content regardless of whether AI was used.
    • User tools/filters to hide AI content and a possible market premium for “no-AI” journalism.