---
license: cc-by-4.0
language:
- en
pretty_name: Incident Audit Fault Attribution
size_categories:
- n<1K
task_categories:
- text-generation
- text-classification
tags:
- accountability
- ai-governance
- incident-audit
- llm-agents
- moral-crumple-zones
---

# Incident Audit Fault Attribution

Dataset accompanying the paper *Separating Governance Designation from Operational Fault in Model-Generated Incident Audits* (Trustworthy AI for Good Workshop (AI4GOOD) at NeurIPS 2026).

Rows and summaries of model-generated incident audits for the main control study and the dose extension, the superseded pilot allocation results that the paper retains for transparency, both study protocols, and the collection and analysis code.

| | |
|---|---|
| **Paper** | [Separating Governance Designation from Operational Fault in Model-Generated Incident Audits](https://openreview.net/forum?id=oZDf3mJY4F) |
| **Venue** | Trustworthy AI for Good Workshop (AI4GOOD) at NeurIPS 2026 |
| **Authors** | Carlos Toxtli, Manuel Delaflor |
| **License** | Creative Commons Attribution 4.0 International (`cc-by-4.0`); code: MIT License |
| **Dataset DOI** | [10.5281/zenodo.24179054](https://doi.org/10.5281/zenodo.24179054) |
| **Version** | 1.0.0 |
| **Maintainer** | [Human-AI Empowerment Lab, Clemson University](https://haielab.org) |

## Paper abstract

When a language model drafts an incident audit, it may conflate the party that caused a failure with the party a governance charter makes answerable for it. We test that distinction in a construct-separated, artifact-complete, model-only audit spanning fixed incidents, several governance descriptions, and three model families. The models consistently track the stipulated source of operational fault while assigning greater governance accountability to the explicitly designated bearer. Changing that bearer moves governance ratings in the prespecified direction without collapsing them into causal contribution or operational fault. Absolute AI designations reach a ceiling, however, so a frozen shared-versus-sole extension distinguishes a dose response for two model families but not for the third. Every request, response, failure, retry, parse, and protocol deviation is retained without repair or renormalization. The audit therefore shows how to test whether model-generated reports preserve a governance distinction under a declared rubric. It does not establish moral judgment, human agreement, legal validity, or deployment benefit.

## Dataset structure

| Path | Format | Size | Records |
|---|---|---|---|
| `code/control_study.py` | py | 16.3 KB |  |
| `code/dose_study.py` | py | 5.7 KB |  |
| `code/superseded_pilot/crossed_experiment.py` | py | 7.5 KB |  |
| `code/superseded_pilot/revision_analysis.py` | py | 4.5 KB |  |
| `data/control_study_rows.json` | json | 1.4 MB |  |
| `data/control_study_summary.json` | json | 5.7 KB |  |
| `data/dose_study_rows.json` | json | 523.7 KB |  |
| `data/dose_study_summary.json` | json | 4.0 KB |  |
| `data/superseded_pilot/allocation_results.json` | json | 28.2 KB | 192 |
| `docs/CONTROL_STUDY_PROTOCOL.md` | md | 3.2 KB |  |
| `docs/DOSE_STUDY_PROTOCOL.md` | md | 1.3 KB |  |

11 files, 2.0 MB in total (metadata files excluded). Records are rows for CSV/TSV/JSONL files and list length for JSON arrays.

## Known limitations

- Model-generated audits only; no human judgments of fault.

## Personal and sensitive information

None.

## License

The data and documentation are released under the [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/). Source code files (.ipynb, .js, .m, .py, .sh) are released under the [MIT License](https://opensource.org/licenses/MIT).

## Citation

If you use this dataset, please cite the paper:

```bibtex
@inproceedings{Toxtli2026Governance,
 title = {Separating Governance Designation from Operational Fault in Model-Generated Incident Audits},
 author = {Toxtli, Carlos and Delaflor, Manuel},
 booktitle = {Trustworthy AI for Good Workshop (AI4GOOD) at NeurIPS 2026},
 address = {Paris, France},
 year = {2026},
 month = dec,
 note = {Poster},
 url = {https://openreview.net/forum?id=oZDf3mJY4F}
}
```

and the dataset:

```bibtex
@dataset{Toxtli2026IncidentAuditFaultAttribData,
  author = {Toxtli, Carlos and Delaflor, Manuel},
  title = {{Incident Audit Fault Attribution}: Dataset for ``Separating Governance Designation from Operational Fault in Model-Generated Incident Audits''},
  year = {2026},
  version = {1.0.0},
  publisher = {Zenodo},
  doi = {10.5281/zenodo.24179054},
  url = {https://doi.org/10.5281/zenodo.24179054}
}
```

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