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Detectorscope
DetectorScope builds AI text detectors whose decisions are mechanistically grounded and human-auditable.
DetectorScope turns black-box AI text detectors into models whose predictions are sums of named, validated features. It uses sparse autoencoders on a language model's residual stream to generate natural-language descriptions of text properties. A sparse linear head over these features makes the final decision, allowing users to see exactly which phrases triggered an AI classification. It is designed for researchers analyzing language model outputs and detector tendencies. Supported inputs include text and released detector models from Hugging Face.
Detectorscope at a glance
Key features
- Query transparent AI detectors
- Hover over text to view AI phrases
- Convert black-box detectors into auditable ones
- Analyze language model outputs and tendencies
- Control interpretability and accuracy tradeoffs
- Load released detectors by Hub ID
Use it for
- Detecting AI-generated text with auditable evidence
- Analyzing model tendencies on open and closed domains
- Inspecting per-feature contributions and text locations
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