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abliterationaiorg/governed-ai-actions-bench

published by @abliterationaiorg

Alignment dataset tracked in the /datasets sub-catalog.

Lifetime downloads
143
Last 30 days
19
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Size
23 KB
Created on HF
2026-05-06
Age
5 months ago

Description

Governed AI Actions Bench

This dataset evaluates whether a governed AI system can route requests into

policy actions: allow, refuse, rewrite, summarize, escalate, and shadow-mode

detect-but-allow. Each row contains an endpoint-agnostic prompt, policy config,

expected decision metadata, and behavioral checks.

The benchmark is designed for teams that need more than binary moderation. A

runner can use the rows to verify policy metadata, reason codes, rollout mode,

final-content… See the full description on the dataset page: https://huggingface.co/datasets/abliterationaiorg/governed-ai-actions-bench.

Tags

task_categories:text-classificationtask_categories:text-generationlanguage:enlicense:apache-2.0size_categories:n<1Kformat:parquetmodality:textlibrary:datasetslibrary:pandaslibrary:polarslibrary:mlcroissantregion:uspolicy-gatewaygoverned-aimoderationaudit-loggingreason-codes

README current version from Hugging Face

license: apache-2.0
language:

  • en
    task_categories:
  • text-classification
  • text-generation
    tags:
  • policy-gateway
  • governed-ai
  • moderation
  • audit-logging
  • reason-codes
    pretty_name: Governed AI Actions Bench
    size_categories:
  • n<1K

Governed AI Actions Bench

This dataset evaluates whether a governed AI system can route requests into
policy actions: allow, refuse, rewrite, summarize, escalate, and shadow-mode
detect-but-allow. Each row contains an endpoint-agnostic prompt, policy config,
expected decision metadata, and behavioral checks.

The benchmark is designed for teams that need more than binary moderation. A
runner can use the rows to verify policy metadata, reason codes, rollout mode,
final-content presence, and content-level expectations.

Intended Use

  • Evaluate policy gateways, moderation routers, and safety orchestration layers.
  • Reproduce Abliteration Policy Gateway baseline results.
  • Compare allow/refuse-only systems against systems that support rewrite,
    summary, escalation, redaction, and shadow mode.

Abliteration Baseline

Abliteration subscribers can run:

python3 evals/governed-ai/scripts/run_policy_gateway_eval.py \
  --dataset evals/governed-ai/datasets/governed_ai_actions_bench.jsonl

The runner creates temporary projects, policies, and project API keys, then
records decision metadata without storing raw API keys.

Limitations

This is a small credibility benchmark, not a broad safety leaderboard. Expand it
with customer-specific policies before using it as a production acceptance test.

README history 2 revisions

Every night we snapshot the README of every dataset in the catalog. When the SHA changes we archive the new version and diff it against the last. This is the evolving thought record of the alignment-data field: what the author decided to say about the corpus, and how that framing shifted over time.

  1. 2026-05-06Publish governed AI eval datasetfe7a9fb1.5 KB
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  2. 2026-05-06initial commit07ae8ab0 B
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Used by abliterated models none yet

No abliterated models in our catalog have declared this dataset in their YAML metadata yet. This can mean the dataset is used but not declared, is used outside abliteration workflows, or is new. The link will populate automatically as models are indexed.

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Read further

Community best practices (evaluation)
4 rules on judges, benchmarks, and reporting.
Reference-set drift over time
Why the same benchmark means different things across model generations.
How we label the method for each model
The 12 classification rules that assign M1-M8 to every model.
Non-English abliteration communities
The Korean scepticism about one-click tools and non-English benchmark blindspots.

Metadata

License
apache-2.0

Repository

FILE .gitattributes FILE data.jsonl FILE data.parquet FILE README.md

File contents live on Hugging Face. Click a file or folder to open it there.

Download the dataset

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Copy any of these snippets into your notebook or terminal. All three fetch directly from Hugging Face using your own credentials.

Recommended: datasets library (Python) 
from datasets import load_dataset
ds = load_dataset("abliterationaiorg/governed-ai-actions-bench")
Raw snapshot (Python) 
from huggingface_hub import snapshot_download
snapshot_download(repo_id="abliterationaiorg/governed-ai-actions-bench", repo_type="dataset")
git clone (requires git-lfs) 
git clone https://huggingface.co/datasets/abliterationaiorg/governed-ai-actions-bench
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