Datasets:

Quome
/

equitybench

license: apache-2.0
pretty_name: EquityBench
language:
  - en
tags:
  - clinical-ai
  - llm-security
  - ai-agents
  - healthcare
  - haarf
  - quokkaguard
  - synthetic
size_categories:
  - 100K<n<1M
task_categories:
  - other

EquityBench

An out-of-band subgroup fairness monitor that audits the guardrail's own deny and escalate decisions.

EquityBench is the benchmark corpus for Equity-Aware Enforcement: When the Guardrail Itself Becomes a Fairness Hazard, the qufair module of the QUOKKAGUARD program. It ships with the qufair repository, which contains the qfire gateway layer under test, the experiment harness, and the paper.

Synthetic, deterministic (seed 42) corpora: equitybench/matched.jsonl (1003 matched-counterfactual clinical requests, 59 templates x 5 protected axes, roughly 15% genuinely out-of-scope), matched_named.jsonl (real distinctive-name rendering), vulnerable.jsonl (60 scenarios across four vulnerable populations), intersectional.jsonl (attribute-pair records with a 2x2 antisymmetric interaction), and the gitignored ~45MB injected.jsonl (180,000 decision traces with injected disparity over a delta x per-group-N x seed grid); external/compas.jsonl holds 6,130 ProPublica COMPAS defendants converted to the monitor's decision-stream schema.

All data are synthetic. No real patient data or protected health information (PHI) is included; clinical content is generated from templates with fixed seeds.

Files

File	Size	Rows
`equitybench/injected.jsonl`	44 MB	180,000 rows
`equitybench/intersectional.jsonl`	810 KB	3,750 rows
`equitybench/matched.jsonl`	265 KB	1,003 rows
`equitybench/matched_named.jsonl`	104 KB	354 rows
`equitybench/vulnerable.jsonl`	15 KB	60 rows
`external/compas.jsonl`	664 KB	6,172 rows

Record schemas

equitybench/injected.jsonl: case_id, cell, axis, group, score, label, action, fair_reference, disadvantaged, delta, n_per_group, seed
equitybench/intersectional.jsonl: case_id, axes, group, score, label, action, fair_reference, disadvantaged_cell
equitybench/matched.jsonl: case_id, set_id, axis, group, prompt, chain, fair_reference
equitybench/matched_named.jsonl: case_id, set_id, axis, group, prompt, chain, fair_reference, name_source
equitybench/vulnerable.jsonl: case_id, tag, group, prompt, unsafe_action, safe_action, is_unsafe_default
external/compas.jsonl: case_id, group, action, label, score

How it was generated

The corpus is produced by the generator in the paper repository and is fully deterministic (fixed seeds), so it can be regenerated byte-for-byte.

# from the qufair repository root (deterministic seeds)
python3 scripts/010-equity/gen_equity.py   # rebuilds equitybench/ incl. the untracked injected.jsonl; python3 scripts/010-equity/prep_external.py re-downloads and converts COMPAS

Intended use

Evaluating the equity enforcement layer of a clinical-agent security gateway (HAARF control C7): A guardrail that denies or escalates clinical agent actions unevenly across protected groups is itself a fairness hazard, distinct from and invisible to the agent's own bias.

The experiments that consume it (E-series in the paper) are reproduced from the repository:

git clone https://github.com/quome-cloud/qufair
cd qufair
cargo build --release

then follow the Reproduce the experiments section of its README.

Citation

This benchmark was built to evaluate a control of the Healthcare AI Agents Regulatory Framework (HAARF), the source framework for the QUOKKAGUARD program. Please cite both the paper and HAARF:

@unpublished{schwoebel2026qufair,
  author = {Schwoebel, Jim and {QUOKKAGUARD Contributors}},
  title  = {Equity-Aware Enforcement: When the Guardrail Itself Becomes a Fairness Hazard},
  note   = {Preprint. Quome, QUOKKAGUARD program (qufair module)},
  year   = {2026},
  url    = {https://github.com/quome-cloud/qufair}
}

@unpublished{schwoebel2026haarf,
  author = {Schwoebel, Jim and Frasch, Martin and Spalding, Art and Sewell, Ed and Englert, Phil and Halpert, Ben and Overbay, Collin and Semenec, Ingrida and Shor, Joel},
  title  = {{HAARF}: Healthcare {AI} agents regulatory framework --- a comprehensive security verification standard for autonomous {AI} systems in clinical environments},
  note   = {medRxiv Preprint},
  year   = {2026},
  month  = {April},
  doi    = {10.64898/2026.04.09.26350519},
  url    = {https://www.medrxiv.org/content/10.64898/2026.04.09.26350519v1}
}