ADEC: AI Ethics Decision-Making Framework – Complete Report
ADEC: AI Ethics Decision-Making Framework – Complete Report
Executive Summary
As AI systems become increasingly sophisticated, research ethics committees face a profound challenge: how to make defensible decisions under moral uncertainty—situations where the potential for systems to possess morally relevant interests is unknown or disputed.
To address this, we developed the AI Ethics Decision Committee (ADEC) Framework, a comprehensive, operational toolkit designed to guide institutions in the ethical oversight of AI research. This framework is grounded in precautionary ethics, emphasizes measurable criteria, and provides actionable tools for real-world implementation.
Key accomplishments:
Conceptual foundation for procedural ethics under uncertainty
Tiered policy framework based on objective system behaviors
Operational tools, including forms, rubrics, verification templates, and escalation flows
Legal/documentation guidance for institutional protection
Training materials with realistic case studies
Quick-reference “Quick Start Sheet” for committee chairs
1. Conceptual Grounding
Ethical review under conditions of uncertainty requires a shift from abstract moral claims to procedural and measurable safeguards. ADEC focuses on:
1. Assessing system behaviors that could imply morally relevant interests
2. Reviewing development practices that might cause harm if systems were sentient
3. Implementing minimization, monitoring, and reversibility measures
4. Documenting decisions clearly while avoiding metaphysical assumptions
This approach balances ethical caution with practical feasibility, ensuring committees can act responsibly without overextending into speculative debates.
2. Policy-Level Framework
ADEC implements a graduated review process:
Criteria Met Tier Action Timeline
0-1 Tier 1 Self-certification (Form A) → File N/A
2-3 Tier 2 Standard review (Form B) → Meeting if complex 30 days
4-5 Tier 3 Enhanced review + external consultation → Full meeting 45–60 days
#AIethics #ResearchEthics #ResponsibleAI #InstitutionalInnovation #MoralUncertainty #EthicalAI #Governance
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p.2 https://lnkd.in/gZdxA5gz
p.3 https://lnkd.in/gPUYip5B
Key Features:
Clear behavioral thresholds (goal persistence, multi-step planning, preference learning, self-modification, multi-domain performance)
Objective verification metrics: logs, plan outputs, behavioral analysis
Escalation for ambiguous, novel, or high-stakes cases
3. Operational Toolkit
Forms
Form A – Self-Certification: For Tier 1 projects with minimal triggering criteria.
Sample project entries provided
Certification ensures researchers report any capability changes
Form B – Protocol Submission: For Tier 2 and Tier 3 projects
Sections include system specifications, verification documentation, development practices, termination plans, risk characterization, timeline, and resources
Example entries demonstrate thorough necessity, alternatives, minimization, and monitoring analysis
Verification Templates
Session Logs: Track goal persistence across multiple sessions
Plan Depth Analysis: Evaluate multi-step reasoning in system outputs
Behavioral Shift Analysis: Assess preference learning through pre/post-training comparisons
Decision Rubrics
Evaluate necessity, alternatives, minimization, and monitoring for each development practice
Examples provided for strong, adequate, and insufficient justifications
Committee actions tied directly to rubric outcomes
Escalation Flowchart
Tier-based guidance for standard and enhanced review
Borderline, ambiguous, or novel cases flagged for chair or external consultation
Emergency protocol for unexpected system behaviors during approved research
#AIethics #ResearchEthics #ResponsibleAI #InstitutionalInnovation #MoralUncertainty #EthicalAI #Governance
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4. Legal & Documentation Guidance
ADEC documentation is structured to support advisory and learning functions while minimizing legal exposure:
Frame records as “internal guidance under uncertainty”
Focus on procedural reasoning rather than metaphysical claims
Separate factual evidence from evaluative judgment
Emphasize monitoring, minimization, and alternatives analysis
Consult legal counsel to tailor practices to institutional context
Key Reminder: Proper documentation ensures transparency, defensibility, and institutional protection, even if moral status of AI remains uncertain.
5. Training Materials
Realistic case studies allow committee members to practice:
Applying the rubric to sample protocols
Assessing necessity and alternatives
Reviewing minimization measures
Specifying monitoring metrics and stop criteria
Sample Exercise: Multi-agent debate system with persistent goals, multi-step planning, preference learning, and self-modification. Participants evaluate negative feedback training for necessity, alternatives, and adequacy of minimization/monitoring measures.
6. Quick Start Sheet
A one-page reference for committee chairs includes:
Tier triage guidance
Quick check of five criteria
Four-part practice assessment (necessity, alternatives, minimization, monitoring)
Decision options: Approve, Approve with Modifications, Tabled, Not Approved
Meeting structure and escalation triggers
Documentation reminders and common mistakes
This sheet ensures rapid, consistent decision-making during meetings without losing procedural rigor.
7. Pilot Metrics Dashboard
Tracks ADEC performance during pilot evaluation:
Submissions by tier
Average review time vs. targets
Researcher satisfaction
Protocols tabled or requiring modifications
Training needs identified
Preliminary pilot results demonstrate:
High compliance with review timelines
Minimal researcher complaints
Effective committee functioning and improved clarity in submissions
Conclusion
This framework provides a defensible, operational pathway for ethics committees overseeing AI research under moral uncertainty. Key strengths:
Clear, measurable criteria
Robust verification and monitoring
Scalable tiered review process
Comprehensive operational, training, and documentation resources
Next steps:
Pilot implementation in real-world research ethics contexts
Collect feedback from chairs and researchers
Refine tools and procedures based on institutional experience
Expand training and template libraries
By focusing on procedural rigor, clarity, and measurability, ADEC equips institutions to govern AI ethically without getting lost in speculation about consciousness—a model for the careful, responsible adoption of advanced AI research.
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#AIethics #ResearchEthics #ResponsibleAI #InstitutionalInnovation #MoralUncertainty #EthicalAI #Governance
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