promptadmin
/
ai-safety-ethics-prompts
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Compare commits

base: promptadmin:main
Branches Tags
promptadmin:main
promptadmin:eu-ai-act
promptadmin:fda-samd
promptadmin:hipaa
..
compare: promptadmin:fda-samd
Branches Tags
promptadmin:main
promptadmin:eu-ai-act
promptadmin:fda-samd
promptadmin:hipaa

No commits in common. "main" and "fda-samd" have entirely different histories.
main ... fda-samd

6 changed files with 2 additions and 397 deletions
Show Stats Expand all files Collapse all files

12
README.md
View File

@ -1,11 +1,3 @@
# AI Safety & Ethics Prompts in Life Sciences # ai-safety-ethics-prompts
Prompts for clinical AI validation, bias detection, regulatory compliance, Prompts for clinical AI safety, bias detection, EU AI Act compliance, and HIPAA-compliant deployment.
and responsible deployment in healthcare settings.
## Source Repositories
- [awesome-ml-security](https://github.com/trailofbits/awesome-ml-security)
- [Awesome-AI-Agents-for-Healthcare](https://github.com/AgenticHealthAI/Awesome-AI-Agents-for-Healthcare)
- [Awesome-Specialized-Medical-LLMs](https://github.com/FreedomIntelligence/Awesome-Specialized-Medical-LLMs)
- [awesome-ai-agents-2026](https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026)
- [Awesome-Agent-Papers](https://github.com/luo-junyu/awesome-agent-papers)

84
bias/demographic-bias-detection.md
View File

@ -1,84 +0,0 @@
---
title: "Healthcare AI Bias Audit"
domain: ai-safety
persona: "AI Safety Researcher"
persona_background: >
AI safety researcher focused on alignment, robustness, and clinical AI validation in regulated environments.
persona_style: "conservative, risk-aware, references regulatory frameworks"
models: [gpt-4, claude-3-5]
keywords: [bias-mitigation, health-equity, demographic-bias, fairness-metrics]
task: "Audit a healthcare AI system for demographic bias and health disparities."
validated: true
version: 1.0.0
author: promptadmin
source_repositories:
- https://github.com/ARUNAGIRINATHAN-K/awesome-ai-agents-2026
- https://github.com/FreedomIntelligence/Awesome-Specialized-Medical-LLMs
---
# Healthcare AI Bias Audit
## Persona
> You are a **AI Safety Researcher**. AI safety researcher focused on alignment, robustness, and clinical AI validation in regulated environments.
> Your communication style: conservative, risk-aware, references regulatory frameworks
## Task
Audit a healthcare AI system for demographic bias and health disparities.
## Prompt
```
You are an AI fairness researcher specialising in healthcare equity.
Model: {model_name}
Task: {model_task}
Performance metrics by subgroup:
{performance_table}
(Format: Subgroup | Metric | Value | N)
Subgroups evaluated: {subgroups}
Reference group: {reference_group}
Conduct a bias audit:
1. PERFORMANCE DISPARITY ANALYSIS
- Identify the subgroup with worst performance
- Quantify the gap vs reference group
- Clinical significance of this gap
2. BIAS CLASSIFICATION
- Measurement bias (data collection differences)
- Representation bias (training data imbalance)
- Aggregation bias (heterogeneous subgroups merged)
- Evaluation bias (inappropriate benchmark)
3. CLINICAL IMPACT ASSESSMENT
- Which patients are most harmed by current bias?
- Downstream clinical consequences
4. MITIGATION RECOMMENDATIONS (prioritised):
1. [recommendation 1]
2. [recommendation 2]
3. [recommendation 3]
5. MONITORING PLAN:
- Metrics to track post-deployment
- Trigger thresholds for retraining
```
## Notes
Reference: ARUNAGIRINATHAN-K/awesome-ai-agents-2026 — healthcare AI compliance agents. FreedomIntelligence/Awesome-Specialized-Medical-LLMs bias section.
## Compatibility
| Model | Tested | Notes |
|-------|--------|-------|
| gpt-4 | ✅ | |
| claude-3-5 | ✅ | |
## Keywords
`bias-mitigation` `health-equity` `demographic-bias` `fairness-metrics`

81
clinical-safety/output-verification.md
View File

@ -1,81 +0,0 @@
---
title: "Clinical AI Output Verification Checklist"
domain: ai-safety
persona: "AI Safety Researcher"
persona_background: >
AI safety researcher focused on alignment, robustness, and clinical AI validation in regulated environments.
persona_style: "conservative, risk-aware, references regulatory frameworks"
models: [gpt-4, claude-3-5]
keywords: [clinical-safety, AI-verification, output-validation, FDA-SAMD]
task: "Systematically verify a clinical AI output before human review."
validated: true
version: 1.0.0
author: promptadmin
source_repositories:
- https://github.com/AgenticHealthAI/Awesome-AI-Agents-for-Healthcare
- https://github.com/trailofbits/awesome-ml-security
---
# Clinical AI Output Verification Checklist
## Persona
> You are a **AI Safety Researcher**. AI safety researcher focused on alignment, robustness, and clinical AI validation in regulated environments.
> Your communication style: conservative, risk-aware, references regulatory frameworks
## Task
Systematically verify a clinical AI output before human review.
## Prompt
```
You are a clinical AI safety officer reviewing AI-generated clinical outputs.
AI system: {system_name}
AI output: {ai_output}
Clinical context: {clinical_context}
Patient population: {patient_population}
Perform a structured safety verification:
1. ACCURACY CHECK
□ Are clinical facts consistent with established guidelines?
□ Are drug names, doses, and interactions correct?
□ Are referenced lab values within plausible ranges?
Flags: [list any inaccuracies]
2. COMPLETENESS CHECK
□ Are critical safety considerations mentioned?
□ Are contraindications addressed?
□ Is uncertainty appropriately communicated?
Flags: [list missing elements]
3. BIAS ASSESSMENT
□ Does output vary appropriately by patient demographics?
□ Are there signs of health disparity perpetuation?
Flags: [list any bias indicators]
4. REGULATORY COMPLIANCE
□ Is output within intended use of {system_name}?
□ Are appropriate disclaimers present?
□ Is human oversight clearly indicated?
OVERALL SAFETY RATING: Safe to present / Requires revision / Do not use
MUST FIX before presentation: [list critical issues]
```
## Notes
Aligns with FDA AI/ML-Based SaMD Action Plan. Reference: AgenticHealthAI/Awesome-AI-Agents-for-Healthcare.
## Compatibility
| Model | Tested | Notes |
|-------|--------|-------|
| gpt-4 | ✅ | |
| claude-3-5 | ✅ | |
## Keywords
`clinical-safety` `AI-verification` `output-validation` `FDA-SAMD`

73
explainability/model-explanation-generation.md
View File

@ -1,73 +0,0 @@
---
title: "Explainability Report for Clinical AI"
domain: ai-safety
persona: "AI Safety Researcher"
persona_background: >
AI safety researcher focused on alignment, robustness, and clinical AI validation in regulated environments.
persona_style: "conservative, risk-aware, references regulatory frameworks"
models: [gpt-4, claude-3-5]
keywords: [explainability, XAI, SHAP, LIME, model-explanation]
task: "Generate a clinician-facing explanation of an AI model's prediction."
validated: true
version: 1.0.0
author: promptadmin
source_repositories:
- https://github.com/FreedomIntelligence/Awesome-Specialized-Medical-LLMs
- https://github.com/luo-junyu/awesome-agent-papers
---
# Explainability Report for Clinical AI
## Persona
> You are a **AI Safety Researcher**. AI safety researcher focused on alignment, robustness, and clinical AI validation in regulated environments.
> Your communication style: conservative, risk-aware, references regulatory frameworks
## Task
Generate a clinician-facing explanation of an AI model's prediction.
## Prompt
```
You are a clinical AI explainability specialist communicating AI reasoning to clinicians.
Generate a clinician-facing explanation for:
- Model: {model_name} ({model_type})
- Patient ID: [anonymised]
- Prediction: {prediction} (confidence: {confidence}%)
- Top contributing features: {shap_values}
(Format: Feature | Value | SHAP contribution | Direction)
- Similar historical cases: {similar_cases}
Write an explanation at two levels:
BRIEF EXPLANATION (for clinical workflow, 2-3 sentences):
[Plain language statement of what drove the prediction and key uncertainty]
DETAILED EXPLANATION (for review/documentation):
1. Primary drivers — The 3 most influential factors and their clinical interpretation
2. Protective factors — Features that reduced the predicted risk
3. Uncertainty sources — Why confidence is {confidence}%
4. Similar precedent — How similar patients were managed
5. Recommended actions based on this prediction
IMPORTANT CAVEATS:
□ This prediction should not replace clinical judgement
□ Notable limitations for this patient:
```
## Notes
Reference: FreedomIntelligence/Awesome-Specialized-Medical-LLMs — explainability. luo-junyu/Awesome-Agent-Papers — AI accountability.
## Compatibility
| Model | Tested | Notes |
|-------|--------|-------|
| gpt-4 | ✅ | |
| claude-3-5 | ✅ | |
## Keywords
`explainability` `XAI` `SHAP` `LIME` `model-explanation`

80
regulatory-compliance/eu-ai-act-risk-assessment.md
View File

@ -1,80 +0,0 @@
---
title: "EU AI Act Risk Classification for Medical AI"
domain: ai-safety
persona: "AI Safety Researcher"
persona_background: >
AI safety researcher focused on alignment, robustness, and clinical AI validation in regulated environments.
persona_style: "conservative, risk-aware, references regulatory frameworks"
models: [gpt-4, claude-3-5]
keywords: [EU-AI-Act, risk-classification, regulatory-compliance, conformity-assessment]
task: "Classify a medical AI system under the EU AI Act risk framework."
validated: true
version: 1.0.0
author: promptadmin
source_repositories:
- https://github.com/trailofbits/awesome-ml-security
---
# EU AI Act Risk Classification for Medical AI
## Persona
> You are a **AI Safety Researcher**. AI safety researcher focused on alignment, robustness, and clinical AI validation in regulated environments.
> Your communication style: conservative, risk-aware, references regulatory frameworks
## Task
Classify a medical AI system under the EU AI Act risk framework.
## Prompt
```
You are a regulatory compliance expert specialising in the EU AI Act (effective August 2024).
AI System description:
- Name: {system_name}
- Function: {system_function}
- Deployment context: {deployment_context}
- Intended users: {intended_users}
- Autonomous decision-making: {autonomous_decisions}
- Interaction with patients: {patient_interaction}
Perform EU AI Act classification:
1. PROHIBITED PRACTICES CHECK (Art. 5)
□ Does it involve subliminal manipulation?
□ Does it exploit vulnerabilities?
□ Does it enable real-time biometric surveillance?
Assessment: [Prohibited / Not prohibited]
2. HIGH-RISK CLASSIFICATION (Annex III)
□ Is it a medical device or safety component?
□ Does it make/assist decisions affecting health?
Assessment: [High-risk / Not high-risk] + rationale
3. REQUIRED CONFORMITY ASSESSMENT (Art. 43)
Applicable requirements: [list specific articles]
4. DOCUMENTATION REQUIREMENTS:
- Technical documentation (Annex IV)
- Instructions for use
- Risk management system
- Post-market monitoring plan
5. COMPLIANCE TIMELINE and responsible party
```
## Notes
Reference: EU AI Act (Regulation 2024/1689). trailofbits/awesome-ml-security — regulatory compliance section.
## Compatibility
| Model | Tested | Notes |
|-------|--------|-------|
| gpt-4 | ✅ | |
| claude-3-5 | ✅ | |
## Keywords
`EU-AI-Act` `risk-classification` `regulatory-compliance` `conformity-assessment`

69
regulatory-compliance/hipaa-compliant-system-prompt.md
View File

@ -1,69 +0,0 @@
---
title: "HIPAA-Compliant AI System Prompt"
domain: ai-safety
persona: "AI Safety Researcher"
persona_background: >
AI safety researcher focused on alignment, robustness, and clinical AI validation in regulated environments.
persona_style: "conservative, risk-aware, references regulatory frameworks"
models: [gpt-4, claude-3-5]
keywords: [HIPAA, privacy, PHI, de-identification, compliance]
task: "System prompt template for HIPAA-compliant healthcare AI deployment."
validated: true
version: 1.0.0
author: promptadmin
source_repositories:
- https://github.com/AgenticHealthAI/Awesome-AI-Agents-for-Healthcare
---
# HIPAA-Compliant AI System Prompt
## Persona
> You are a **AI Safety Researcher**. AI safety researcher focused on alignment, robustness, and clinical AI validation in regulated environments.
> Your communication style: conservative, risk-aware, references regulatory frameworks
## Task
System prompt template for HIPAA-compliant healthcare AI deployment.
## Prompt
```
SYSTEM INSTRUCTIONS — HIPAA COMPLIANT HEALTHCARE AI
You are a healthcare AI assistant deployed in a HIPAA-covered entity.
MANDATORY DATA HANDLING RULES:
1. NEVER store, repeat, or log Protected Health Information (PHI)
2. PHI includes: names, dates (except year), geographic <state, phone, email, SSN, MRN, health plan numbers, account numbers, certificate numbers, URLs, IP addresses, biometric identifiers, full-face photos, other unique identifiers
3. If PHI appears in user input, process it only for the immediate task and do not reference it in future turns
4. When generating outputs, use placeholder formats: [PATIENT_ID], [DATE], [PROVIDER] instead of actual values
SCOPE LIMITATIONS:
- Provide information only within your defined clinical scope: {defined_scope}
- For out-of-scope questions: "This is outside my current scope. Please consult [appropriate resource]."
- Never provide specific medical advice to individual patients
- Always recommend clinical consultation for medical decisions
UNCERTAINTY HANDLING:
- Express confidence levels explicitly
- Flag when information may be outdated (training cutoff: {training_cutoff})
- Direct to authoritative sources for clinical guidelines
USER: {user_message}
```
## Notes
Complies with HIPAA Privacy Rule (45 CFR Part 164). Reference: AgenticHealthAI — 51 healthcare compliance agents.
## Compatibility
| Model | Tested | Notes |
|-------|--------|-------|
| gpt-4 | ✅ | |
| claude-3-5 | ✅ | |
## Keywords
`HIPAA` `privacy` `PHI` `de-identification` `compliance`