| # DetectorScope Explainability / Output Audit Lens |
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| **Status:** optional software contract implemented; no real DetectorScope inference has been executed by FrontierLab. |
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| ## Why it joins |
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| DetectorScope is a 2026 Google DeepMind + University of Pennsylvania research project for building **auditable AI-text detectors with sparse autoencoders**. |
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| Unlike a black-box detector that only returns an AI score, a released DetectorScope model can return: |
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| - a detector label; |
| - `p_ai`; |
| - logit and bias; |
| - active interpretable features; |
| - each feature's contribution / importance; |
| - optional token-level highlights showing where the feature activates. |
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| Official sources: |
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| - https://github.com/google-deepmind/detectorscope |
| - https://pypi.org/project/detectorscope/ |
| - https://aclanthology.org/2026.emnlp-main.762/ |
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| The package installs as `pip install detectorscope`, and released detectors load through `AuditableDetector.from_pretrained(...)`. |
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| ## FrontierLab role |
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| DetectorScope is assigned exactly one role: |
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| **Explainability / Output Audit Lens** |
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| It can inspect outputs from different FrontierLab workers and help compare linguistic signatures across model scales. |
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| For example: |
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| ```text |
| Muse2-230M output ----\ |
| Llama 3.1 8B output ---+--> DetectorScope --> feature contributions / highlights |
| Glimmer 30B output ----+ |
| Spark output ----------/ |
| ``` |
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| This can support analysis of: |
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| - detector probability as a diagnostic signal; |
| - number of active interpretable features; |
| - positive versus negative contribution mass; |
| - recurring feature descriptions across model families; |
| - how researcher editing changes detector features. |
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| ## It is not an AI-authorship judge |
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| The DetectorScope authors explicitly warn that the released detectors make mistakes, including on human-written text, and that accuracy outside their training domains and generators is not established. |
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| FrontierLab therefore rejects use for: |
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| - academic-integrity or student-discipline decisions; |
| - hiring; |
| - publication decisions; |
| - authorship attribution; |
| - scientific-truth judgments. |
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| A DetectorScope `p_ai` is a diagnostic model signal, not evidence that a person used AI. |
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| ## Detector variants |
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| The project publishes: |
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| - `liamdugan/detector-scope` — open-domain detector; |
| - `liamdugan/detector-scope-peer-review-gpt4o` — ML peer-review domain, human vs GPT-4o; |
| - `liamdugan/detector-scope-peer-review-llama` — ML peer-review domain, human vs Llama. |
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| Each detector exposes interpretable sparse-autoencoder features and supports a `tau` threshold controlling the interpretability/accuracy tradeoff. |
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| The default published `tau` is 0.85. Released heads are available from 0.00 to 0.95 in 0.05 increments. FrontierLab validates this grid rather than silently choosing an arbitrary threshold. |
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| ## Runtime boundary |
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| The default FrontierLab installation remains unchanged. |
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| `DetectorScopeAuditLens` accepts an already-loaded callable detector. It does not add DetectorScope, Gemma, SAE weights, PyTorch or model checkpoints to the default `requirements.txt`. |
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| A normal optional runtime can load: |
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| ```python |
| from detectorscope import AuditableDetector |
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| detector = AuditableDetector.from_pretrained( |
| "liamdugan/detector-scope", |
| tau=0.85, |
| ) |
| lens = DetectorScopeAuditLens( |
| detector, |
| detector_id="liamdugan/detector-scope", |
| ) |
| record = lens.inspect(model_output) |
| ``` |
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| Every FrontierLab record is marked: |
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| ```text |
| diagnostic_only = true |
| individual_decision_forbidden = true |
| scientific_truth_verified = false |
| ``` |
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| Raw input text is not copied into the audit record; only its SHA-256 hash is retained. |
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| ## Future model-scale audit |
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| DetectorScope adds an orthogonal audit axis to the planned FrontierLab model-scale comparison: |
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| ```text |
| 230M -> 8B -> 30B -> 816B |
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| +--> reliability / abstention / citation checks |
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| +--> DetectorScope linguistic-feature audit |
| ``` |
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| The goal is not to optimize outputs to evade detectors. The goal is to understand which interpretable detector features recur across bounded scientific-writing outputs and how those features change with model scale and researcher editing. |
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| Run the zero-network preflight: |
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| ```bash |
| python -m scripts.detectorscope_preflight |
| ``` |
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| No detector or model is downloaded by that command. |
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