Evidence-Based Architecture
CouncilIA operationalizes research from multi-agent systems and decision science into a structured deliberation platform. Our approach is scientifically grounded, not scientifically proven.
Multi-Agent Deliberation
Shaikh et al. (PLOS 2025)
Multi-instance LLM deliberation achieves 97% accuracy in high-stakes clinical exams. CouncilIA extends this by replacing identical instances with specialized personas for cognitive diversity.
Adversarial Reasoning
Ellemers et al. (PNAS 2020)
Inspired by research on 'Adversarial Alignment' where structured conflict improves theory-building. CouncilIA operationalizes this through intentional role tension and forced challenges.
Independent Perspectives
Distributed Multi-Agent Systems
Based on game-theoretic principles where independent agents optimize domain-specific criteria. This improves decision robustness by surfacing risks that single-model outputs miss.
Iterative Deliberation
3-Round Optimization
Empirical data suggests diminishing returns beyond a limited round count. CouncilIA adopts a strict Thesis → Antithesis → Synthesis protocol for maximum efficiency.
Human-AI Governance
Amershi et al. (CHI 2019)
Implementation of Microsoft Research G11 (Explainability) and G17 (Human Control) guidelines. AI structures decisions, but humans remain final and accountable.
Technical Implementation
Deliberation Trace System
CouncilIA produces a structured decision trace: argument isolation, evidence mapping (RAG), and refinement logs. This enables complete auditability by regulatory bodies.
VaR-Inspired Risk Modeling
Decision uncertainty is quantified through agent disagreement and evidence gaps. High Dissent = High Uncertainty = Mandatory Human Review.
Decision Metrics
Variance between expert outputs
Arguments surviving all rounds
Citations per critical claim
Adherence to regulatory RAG
The 3-Round Protocol
| Round | Purpose | Output |
|---|---|---|
| R1: Thesis | Independent expert evaluation | Domain-specific analysis + Evidence |
| R2: Antithesis | Structured adversarial critique | Unrefuted risks + Direct challenges |
| R3: Synthesis | Evidence-based refinement | Decision document + Action plan |
System Limitations
- Dependent on input quality (Garbage In → Structured Garbage Out)
- Does not guarantee correctness; only de-risks process
- Does not replace domain experts or regulatory sign-off
- Cannot validate outcome without empirical testing
What We Do Not Claim
- ❌ AI replaces human decision-making
- ❌ Universally proven accuracy
- ❌ Elimination of risk
"We structure decisions. Humans remain accountable."