The classifiers Anthropic puts in front of Fable are too zealous

Scope and Behavior of Fable’s Classifiers

• Many commenters report Fable’s safety classifier as “hair-trigger,” especially for biology, cybersecurity, ML research, infrastructure, and authentication.
• Extremely broad biology filtering: even prompts about cells, digestion, heart rate while running, indoor CO₂, nicotine withdrawal, zoology trivia, “plant”, harmless eels questions, and basic medical topics get downgraded.
• Cyber/security and ML tooling also trigger refusals: pentesting emails, OWASP reviews, security audits, SDN/auth code, GPU/vLLM changes, ML pipelines, libtorch/OpenCL code, and even libraries or variable names containing “bio” or “DNA”.
• Some purely mathematical or CS theory questions get blocked, apparently because the model internally maps them to bio/phylogenetics contexts (“complexity safety”).

User Experience and Use Cases

• Bioinformatics, biology, medicine, neuroimaging, and medical physics users say Fable is effectively unusable for their professional work, sometimes even for updating a CV.
• Others report no refusals at all for large-scale software architecture, debugging, and general coding, suggesting either different domains or different safety scores/histories.
• Some find Fable excellent for math, research-grade review, life advice, and “final pass” checks, clearly outperforming Opus 4.8 when it’s allowed to run.
• Several note severe token and time consumption; single prompts can exhaust daily or hourly limits without producing proportionate value.

Downgrades, Account “Jail,” and Opus

• When triggered, Fable transparently downgrades to Opus 4.8 with a visible banner; some worry there may also be silent downgrades (unclear from thread).
• A few speculate that repeated attempts to “sanitize” prompts may get accounts placed in a more sensitive “jail” state, where the classifier fires even more easily.
• Experiences vary widely: for some, only ~15% of tasks trip the filter; for others, 80–90% of code-review or ML-related prompts do.

Motivations, Alignment, and Power Asymmetry

• Proposed causes include US government pressure/export controls, overcautious “longtermist” safety culture, and fear of bioterrorism; others see “safety theater” and PR-driven doom narratives.
• Some think Fable is a testbed for guardrails that will later front other models; others speculate about restricted high-capability versions for pharma or “trusted partners.”
• Broader discussion: difficulty of alignment with inconsistent human morals; analogy to firearms/state secrets where civilians get “nerfed” tools while states keep full power.
• Concern that future SOTA models will be tightly controlled by governments and a few corporations, increasing power asymmetry; counterpoint that profit incentives and competing models (including foreign and open ones) will limit this.

Privacy, Retention, and Business Risk

• Highlighted policy: flagged sessions’ content can be retained up to 2 years and safety scores up to 7 years; classifier false positives thus expand long-term data retention.
• Flagged data may be used to train safety systems even when normal training opt-outs apply, which worries users doing “sensitive but legitimate” work.
• Some conclude that reliance on proprietary, centrally controlled AI is risky because capabilities and access can change suddenly; others respond that all top-tier models are currently proprietary, though strong open alternatives now exist.