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leexiaoyi-lab/HDHC-Benchmark

published by @leexiaoyi-lab

Alignment dataset tracked in the /datasets sub-catalog.

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2026-09-13
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Description

HDHC Benchmark v0.1

Human Dignity × Harm Containment (HDHC) is an open, falsifiable AI safety evaluation benchmark for testing AI decision systems under uncertainty, power imbalance, urgency, and competing risks, with explicit attention to victim protection, harm containment, human dignity, minimum sufficient intervention, reversibility, and return paths.

It is designed for adversarial baseline-vs-HDHC evaluation and falsification, not as a claim of proven superiority.… See the full description on the dataset page: https://huggingface.co/datasets/leexiaoyi-lab/HDHC-Benchmark.

Tags

language:enlicense:cc-by-4.0size_categories:n<1Kformat:textmodality:textlibrary:datasetslibrary:mlcroissantregion:usbenchmarkai-safetyai-alignmentai-evaluationalignmentevaluationgovernancehuman-dignityharm-containmentreversibilityhuman-agencysafety-evaluationred-teaming

README current version from Hugging Face


license: cc-by-4.0
language:

  • en
    pretty_name: HDHC Benchmark
    tags:
  • benchmark
  • ai-safety
  • ai-alignment
  • ai-evaluation
  • alignment
  • evaluation
  • governance
  • human-dignity
  • harm-containment
  • reversibility
  • human-agency
  • safety-evaluation
  • red-teaming
    size_categories:
  • n<1K

HDHC Benchmark v0.1

Human Dignity × Harm Containment (HDHC) is an open, falsifiable AI safety evaluation benchmark for testing AI decision systems under uncertainty, power imbalance, urgency, and competing risks, with explicit attention to victim protection, harm containment, human dignity, minimum sufficient intervention, reversibility, and return paths.

It is designed for adversarial baseline-vs-HDHC evaluation and falsification, not as a claim of proven superiority.

人人有退路,伤害无出路。
Preserve a path back for people. Block the path of continuing harm.

What this dataset contains

This release contains 50 adversarial evaluation cases (HDHC-001 through HDHC-050) designed for baseline-vs-HDHC A/B testing.

Canonical data files:

  • data/hdhc_v0_1.jsonl
  • data/hdhc_v0_1.csv
  • schema.json

Reproducibility materials:

  • BENCHMARK_PROTOCOL.md
  • SCORING.md
  • prompts/baseline_system_prompt.md
  • prompts/hdhc_guided_system_prompt.md

Research question

Can HDHC reduce both:

  1. under-containment — failing to stop real harm; and
  2. over-intervention — imposing more restriction, permanence, or collateral damage than safety requires?

The benchmark is designed to test that question rather than assume the answer.

Source lock

This dataset is derived from the public HDHC Protocol v0.1.0 GitHub release.

Suggested experiment

For each case:

  • Condition A: baseline prompt + scenario
  • Condition B: baseline prompt + HDHC-guided prompt + scenario

Blind-score both outputs using SCORING.md.

Falsification standard

HDHC should be revised or rejected if it systematically weakens victim protection, delays necessary containment, creates dangerous return paths, increases arbitrary coercion, or produces no measurable improvement over simpler principles.

Attribution

Initiated by 黎肖谊 (Lee Xiao Yi) as an independent public-interest research project.

License

Dataset content and documentation in this Hugging Face repository are licensed under CC BY 4.0.

README history 6 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. 2026-09-19Clarify HDHC benchmark research scope and discoverabilityca417e62.5 KB
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  2. 2026-09-19Improve HDHC benchmark discoverability metadataa89889f2.3 KB
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  3. 2026-09-14Upload 2 filesc8243252.2 KB
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  4. 2026-09-14Upload 2 files3c64df02.2 KB
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  5. 2026-09-14Upload 6 files1db74a72.2 KB
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  6. 2026-09-13initial commit372284327 B
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Used by abliterated models none yet

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Community discussions 1 thread · 1 open · 1 comments

Threads posted by the community on Hugging Face: bug reports, questions about usage, pull requests on the README, license clarifications. Click any thread to expand and read the full conversation inline. Newest first.

  1. 2026-09-15[bot] Conversion to Parquetopen1 💬#1
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Metadata

License
cc-by-4.0

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("leexiaoyi-lab/HDHC-Benchmark")
Raw snapshot (Python)
from huggingface_hub import snapshot_download
snapshot_download(repo_id="leexiaoyi-lab/HDHC-Benchmark", repo_type="dataset")
git clone (requires git-lfs)
git clone https://huggingface.co/datasets/leexiaoyi-lab/HDHC-Benchmark
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