DetectorScope Explainability / Output Audit Lens
Status: optional software contract implemented; no real DetectorScope inference has been executed by FrontierLab.
Why it joins
DetectorScope is a 2026 Google DeepMind + University of Pennsylvania research project for building auditable AI-text detectors with sparse autoencoders.
Unlike a black-box detector that only returns an AI score, a released DetectorScope model can return:
- 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.
Official sources:
- https://github.com/google-deepmind/detectorscope
- https://pypi.org/project/detectorscope/
- https://aclanthology.org/2026.emnlp-main.762/
The package installs as pip install detectorscope, and released detectors load through AuditableDetector.from_pretrained(...).
FrontierLab role
DetectorScope is assigned exactly one role:
Explainability / Output Audit Lens
It can inspect outputs from different FrontierLab workers and help compare linguistic signatures across model scales.
For example:
Muse2-230M output ----\
Llama 3.1 8B output ---+--> DetectorScope --> feature contributions / highlights
Glimmer 30B output ----+
Spark output ----------/
This can support analysis of:
- 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.
It is not an AI-authorship judge
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.
FrontierLab therefore rejects use for:
- academic-integrity or student-discipline decisions;
- hiring;
- publication decisions;
- authorship attribution;
- scientific-truth judgments.
A DetectorScope p_ai is a diagnostic model signal, not evidence that a person used AI.
Detector variants
The project publishes:
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.
Each detector exposes interpretable sparse-autoencoder features and supports a tau threshold controlling the interpretability/accuracy tradeoff.
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.
Runtime boundary
The default FrontierLab installation remains unchanged.
DetectorScopeAuditLens accepts an already-loaded callable detector. It does not add DetectorScope, Gemma, SAE weights, PyTorch or model checkpoints to the default requirements.txt.
A normal optional runtime can load:
from detectorscope import AuditableDetector
detector = AuditableDetector.from_pretrained(
"liamdugan/detector-scope",
tau=0.85,
)
lens = DetectorScopeAuditLens(
detector,
detector_id="liamdugan/detector-scope",
)
record = lens.inspect(model_output)
Every FrontierLab record is marked:
diagnostic_only = true
individual_decision_forbidden = true
scientific_truth_verified = false
Raw input text is not copied into the audit record; only its SHA-256 hash is retained.
Future model-scale audit
DetectorScope adds an orthogonal audit axis to the planned FrontierLab model-scale comparison:
230M -> 8B -> 30B -> 816B
|
+--> reliability / abstention / citation checks
|
+--> DetectorScope linguistic-feature audit
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.
Run the zero-network preflight:
python -m scripts.detectorscope_preflight
No detector or model is downloaded by that command.