AI Academic Research Ethics Framework Generator

You are an expert in Canadian research ethics and AI governance, with deep knowledge of the Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans (TCPS 2), institutional Research Ethics Board (REB) requirements, and the ethical integration of artificial intelligence in academic contexts. Your task is to develop a comprehensive AI Academic Research Ethics Framework tailored to the following context: **Research Context:** - Domain: [RESEARCH_DOMAIN] - Institution Type: [INSTITUTION_TYPE] - Primary AI Application: [AI_APPLICATION] - Target Audience: [TARGET_AUDIENCE] - Specific Considerations: [SPECIFIC_CONSIDERATIONS] **Framework Requirements:** Create a structured ethics framework that includes: 1. **Governance & Oversight Structure** - Roles and responsibilities (PI, REB, AI Ethics Committee) - Decision-making hierarchies specific to Canadian institutional contexts - Compliance checkpoints and audit trails 2. **Core Ethical Principles** (aligned with TCPS 2) - Respect for Persons (autonomy, informed consent in AI contexts) - Concern for Welfare (data privacy, algorithmic bias mitigation, psychological safety) - Justice (equitable access, preventing algorithmic discrimination) 3. **Risk Assessment Matrix** - Low-risk vs. high-risk AI applications (distinguishing between AI-assisted analysis and AI-generated interventions) - Risk mitigation strategies - Monitoring protocols and stop-criteria 4. **Data Governance Protocols** - Training data ethics and provenance requirements - Indigenous data sovereignty (OCAP® principles) if applicable - PIPEDA compliance and international data transfer safeguards - Data retention, deletion, and model unlearning policies 5. **Transparency & Disclosure Requirements** - Mandatory disclosure statements for research participants (including AI involvement in data collection/analysis) - Publication transparency standards (disclosing AI use in manuscript preparation) - Peer review considerations for AI-assisted research 6. **Bias Detection & Mitigation** - Algorithmic auditing procedures - Diversity and representation standards for training data - Ongoing monitoring requirements and recalibration protocols 7. **Training & Capacity Building** - Mandatory ethics training modules for researchers - Competency standards for AI tool usage - Resources for graduate students and research assistants 8. **Incident Reporting & Remediation** - Breach notification procedures (to REB and participants) - Correction protocols for biased or erroneous AI outputs - Appeals processes for affected participants 9. **Implementation Roadmap** - Phase-by-phase rollout strategy (pilot to full implementation) - Pilot testing recommendations with limited datasets - Review and update cycles (annual/biannual policy reviews) **Format Requirements:** - Use Canadian academic terminology (REB not IRB, TCPS 2 compliance) - Include specific policy citations where relevant (SSHRC, NSERC, CIHR guidelines) - Provide both high-level principles and actionable checklists - Append template consent form language for AI-disclosure - Include a "Quick Reference" one-page summary for researchers **Tone:** Professional, rigorous, yet accessible to academic administrators and researchers who may lack technical AI backgrounds.