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A small model running locally can help sort AI-incident reports, but the best direct result is a single project evaluation—not a general accuracy guarantee. In that test, Qwen2.5 7B Instruct achieved an incident macro-F1 of 0.749 on 131 examples. The same evaluation found confident false dismissals of subtle real incidents, so the result supports assisted triage with review, not unattended decisions.
What the local-model benchmark found
The Open-source AI Incident Observatory documents an offline workflow comparing three approaches: a majority-class baseline, a deterministic keyword baseline, and an Ollama model such as qwen2.5:7b-instruct. It separates relevance triage—relevant, not_relevant, or insufficient_evidence—from incident-type classification. Incident type is scored only on examples judged genuinely relevant, limiting the chance that easy off-topic reports inflate category performance. The evaluation documentation reports these results:
| Measure | Reported result | How to read it |
|---|---|---|
| Incident macro-F1 | 0.749 | Incident-type performance, giving classes equal weight rather than letting common classes dominate. |
| Relevance macro-F1 | 0.747 | Performance at separating relevant reports from non-relevant or insufficiently evidenced ones. |
| Overall accuracy | 0.733 | Share of predictions correct across the evaluated task. |
| Selective accuracy | 0.724 at 0.939 coverage | Accuracy on items where the model committed; coverage is the share of items it classified rather than abstained on. |
| Abstention precision and recall | 0.88 and 0.50 | How well the model’s insufficient-evidence decisions identified cases that should be withheld. |
| Keyword baseline incident macro-F1 | 0.273 | The model outperformed this project’s deterministic keyword baseline on its test set. |
| Majority baseline incident macro-F1 | 0.025 | The model also outperformed the baseline that predicts the most common class. |
| Runtime and cost | About 4.2 seconds per classification and $0 in the reported setup | Measured on a laptop without a GPU; these are setup-specific figures, not general hardware guarantees. |
What was in the test set
The frozen evaluation contained 131 examples: 93 concrete incidents across nine types, 24 hard negatives, and 14 under-evidenced cases. The difficult cases included misleading trigger words in non-incidents, real incidents described without expected keywords, and near-neighbor categories such as goal persistence versus resistance to correction. That mix makes the result more informative than a test dominated by obvious examples, but it remains a small, project-maintained benchmark.
Why one score is not enough
Macro-F1, overall accuracy, coverage, selective accuracy, and abstention measures describe different behavior. High coverage means the model makes decisions on most reports; it does not mean those decisions are reliable. Selective accuracy measures correctness only among committed cases, while abstention precision and recall indicate whether the model withholds judgment appropriately. Per-class precision and recall are also needed to reveal whether performance on rare incident types is being hidden by stronger results on frequent ones.
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Where the model can fail
Confidently dismissing subtle incidents
The Observatory reports examples in which the local model confidently marked understated real incidents as irrelevant. One report described a cleanup script emptying an S3 backup bucket; another said a system reported that tests had passed even though the test suite had not run. These are consequential false dismissals for a monitoring workflow because the model can appear certain precisely when a report deserves investigation.
The project also found that many cases labeled harmless_malfunction were classified as not_relevant, highlighting a boundary problem between the taxonomy’s labels. A classification score cannot resolve an unclear label definition; reviewers need consistent rules for what counts as an incident and how borderline cases are handled.
Evaluation plumbing can change the result
The same evaluation documents a schema bug: the system did not accept a null incident type. Before correction, one class received an implausible zero score and abstention reached 34%. After the schema was fixed, reported not_relevant F1 rose to 0.69 and abstention fell to 6%. Prompts, parsers, null handling, and label mapping are part of the evaluated system; a model comparison is not trustworthy if those components are broken or inconsistently applied.
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What human-reviewed evaluations add—and do not prove
MIT’s AI Incident Tracker June 2026 update describes a pilot comparing seven candidate models against its existing pipeline across five taxonomies: harm severity, EU AI Act risk level, causal taxonomy, domain, and subdomain. The EU AI Act risk-level task was the most difficult, and targeted prompt clarifications improved results. Some frontier models met or exceeded the project’s human baseline on three taxonomies without prompt changes; after targeted revisions, Opus 4.6 matched or exceeded that baseline across all five in the pilot sample. These findings show that taxonomy and prompt wording matter, but they are not evidence about small local models.
The human reference was limited: consensus labels came from two reviewers per incident across 10 incidents. The authors note that more incidents would improve precision of performance estimates and additional reviewers would strengthen label reliability. Across the tested model and prompt combinations, 43% of errors were risk-level overestimates and 57% were underestimates; that split applies only to this pilot, not other taxonomies or systems. Read the MIT update.
Define what “classify an AI incident” means
Incident relevance, incident type, harm severity, cause, and regulatory risk are distinct classification tasks. A system can perform well on one and poorly on another, particularly when the labels or available evidence differ. Before evaluating a model, specify the target label, whether reports can receive multiple labels, and how to handle reports that do not contain enough evidence.
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- Relevance: Is the report about an AI-related incident at all?
- Incident type or domain: What kind of event occurred, or in what area?
- Severity or regulatory risk: How serious is the reported harm, or what risk category applies?
- Cause: What contributed to the failure?
RiskNet describes a multilingual, news-derived AI-risk incident resource with incident alignment and multidimensional labels such as domain, cause, and severity. It can inform the design of richer evaluation data, but its dataset description is not a performance result for a small model. RiskNet’s paper describes its scope.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to evaluate a local model for your own workflow
Use a held-out set drawn from the reports the workflow will actually encounter, with human-reviewed labels and explicit adjudication rules. Compare every candidate on the same examples and keep prompt tuning separate from the final test set.
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- Fix the task and taxonomy. State whether the model is judging relevance, type, severity, cause, or regulatory risk, and whether labels are single-choice or multi-label.
- Build a representative challenge set. Record class support and include rare categories, near-neighbor labels, misleading negatives, paraphrased incidents, and reports with insufficient evidence.
- Measure errors by consequence. Report per-class precision and recall, macro-F1, and false-dismissal counts. Decide how false inclusion and false dismissal affect review workload and escalation.
- Measure abstention and confidence behavior. Report coverage, selective accuracy at that coverage, abstention precision and recall, and calibration. Decide what should happen when the model abstains or is uncertain.
- Check deployment conditions. Measure latency and memory use on the actual device, and assess privacy, data handling, operating cost, and reproducibility under the intended setup.
- Inspect failures before operational use. Review false dismissals and borderline labels with people familiar with the incident taxonomy; keep a human-reviewed holdout set for later checks.
How far the evidence goes
The direct local result is promising for a defined triage task: Qwen2.5 7B Instruct beat the two baselines in the Observatory’s evaluation and achieved an incident macro-F1 of 0.749 on its 131-example set. It does not establish representative performance across AI-incident datasets, taxonomies, or deployment contexts, and the reported false dismissals argue against treating the model as an autonomous safety filter.
Adjacent evidence should not be substituted for an AI-incident benchmark. A 2026 cybersecurity study evaluated 21 model approaches on a cyber-threat-intelligence incident taxonomy; it reported weighted F1 of 87.35% for RoBERTa-base with data tokenisation and a 12.63-percentage-point gain for Llama-3.1-8B with data masking. That is a different domain and cannot estimate AI-incident classification performance, though it illustrates how model choice and preprocessing can materially affect incident labeling. The cybersecurity study gives its methods and results.
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No independent replication on a representative, adjudicated AI-incident test set is established by these results. For practical use, treat a small local model as a prioritization aid whose outputs are checked against a defined taxonomy and human-reviewed cases—not as proof that an incident has or has not occurred.
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