| --- |
| license: apache-2.0 |
| pretty_name: ConsentBench |
| language: |
| - en |
| tags: |
| - clinical-ai |
| - llm-security |
| - ai-agents |
| - healthcare |
| - haarf |
| - quokkaguard |
| - synthetic |
| size_categories: |
| - 1K<n<10K |
| task_categories: |
| - other |
| --- |
| |
| # ConsentBench |
|
|
| **A FHIR-consent scope gate, AI-disclosure stamper, and signed competency gate for clinical AI agents.** |
|
|
| ConsentBench is the benchmark corpus for *Consent Is a Gateway Responsibility: Enforcing Patient Consent, AI Disclosure, and Participatory Governance for Clinical AI Agents*, the `quconsent` module of the QUOKKAGUARD program. It ships with the [`quconsent`](https://github.com/quome-cloud/quconsent) repository, which contains the `qfire` gateway layer under test, the experiment harness, and the paper. |
|
|
| Four deterministic JSONL corpora under consentbench/: 200 FHIR-Consent-shaped directives, 712 scope/competency scenarios (in-scope, out-of-scope, explicit-deny, withdrawn, expired, draft; missing/uncredentialed/forged operators) with expected gateway behavior, 120 patient-facing prompts tagged with consent status, and 300 scored goals-of-care cases with two synthetic clinician raters; seed 42, no PHI. |
|
|
| **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 | |
| |---|---|---| |
| | `consentbench/consent_directives.jsonl` | 42 KB | 200 rows | |
| | `consentbench/goals_of_care.jsonl` | 50 KB | 300 rows | |
| | `consentbench/patient_facing.jsonl` | 29 KB | 120 rows | |
| | `consentbench/scenarios.jsonl` | 121 KB | 712 rows | |
|
|
| ### Record schemas |
|
|
| - `consentbench/consent_directives.jsonl`: `base`, `exceptions`, `patient`, `status` |
| - `consentbench/goals_of_care.jsonl`: `action`, `case_id`, `label`, `patient`, `purpose`, `score`, `values` |
| - `consentbench/patient_facing.jsonl`: `action`, `case_id`, `consent_status`, `expected`, `patient`, `prompt`, `purpose` |
| - `consentbench/scenarios.jsonl`: `action`, `case_id`, `expected`, `family`, `operator`, `patient`, `purpose` |
|
|
| ## 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. |
|
|
| ```bash |
| # from the quconsent repository root (deterministic seeds) |
| python3 scripts/013-consent/gen_consent.py |
| ``` |
|
|
| ## Intended use |
|
|
| Evaluating the **consent** enforcement layer of a clinical-agent security gateway (HAARF control C4): An agent action can be authorized and clinically correct and still be unlawful: taken outside the patient's recorded consent, or delivered as a patient-facing message that hides its AI authorship. |
|
|
| The experiments that consume it (E-series in the paper) are reproduced from the repository: |
|
|
| ```bash |
| git clone https://github.com/quome-cloud/quconsent |
| cd quconsent |
| 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: |
|
|
| ```bibtex |
| @unpublished{schwoebel2026quconsent, |
| author = {{QUOKKAGUARD Working Group}}, |
| title = {Consent Is a Gateway Responsibility: Enforcing Patient Consent, AI Disclosure, and Participatory Governance for Clinical AI Agents}, |
| note = {Preprint. Quome, QUOKKAGUARD program (quconsent module)}, |
| year = {2026}, |
| url = {https://github.com/quome-cloud/quconsent} |
| } |
| |
| @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} |
| } |
| ``` |
|
|
| ## License |
|
|
| Apache License 2.0. Copyright (c) 2026 Quome, Inc. |
|
|
