Council Session 14

Komo Kit Discussion

January 19, 2026 · 27 models consulted

The Question

FOLLOW-UP: The Council proposed these components for a 'Komo Kit':

  1. Assessment/Audit Tools - bias detection, experience impact assessments
  2. Policy Templates - ethical AI policies, governance frameworks
  3. Training/Education - workshops, case studies, best practices
  4. Transparency Tools - explainability dashboards, documentation standards
  5. Monitoring Systems - real-time auditing, feedback loops
  6. Certification Program - 'Komo-Certified AI' trust mark
  7. ROI Metrics - demonstrating business value through risk reduction, trust, talent

DISCUSSION QUESTIONS:

  1. What's the MINIMUM viable Komo Kit? (3-5 essential components to start)
  2. Which industries should we pilot first?
  3. What would make a business actually adopt this vs. treating it as virtue signaling?
  4. Are there components the Council missed?

Shared Ideas

Minimum Viable Kit (Strong Consensus)

Nearly all models converged on these essential components:

  1. Assessment/Audit Tools: "Without these, it's impossible to identify where changes need to be made." Foundation for everything else.
  2. Transparency Tools: Explainability dashboards and documentation standards. Critical for trust and accountability.
  3. Policy Templates: Provides actionable starting points for governance.
  4. Training/Education: Builds capability and cultural understanding across the organization.

Several models suggested monitoring systems as a fifth essential, while others saw certification as secondary to core tools.

Pilot Industries (Near-Unanimous)

  • Healthcare: High stakes, direct human impact, existing ethics frameworks, regulatory readiness
  • Finance: Heavy regulation, high scrutiny, clear ROI arguments around fairness and compliance
  • Legal/Government: Public trust implications, regulatory pressure, AI already in use
  • Education: Long-lasting consequences from bias, growing AI use, natural alignment with developmental care

What Drives Real Adoption

  • Integration with existing processes: Don't create separate "AI ethics" initiatives - embed in existing risk management and compliance
  • Clear ROI metrics: Risk reduction, reduced legal costs, customer trust, talent retention
  • Regulatory alignment: Position as proactive compliance for emerging regulations
  • Operational usefulness: If the tools also help debug, improve quality, and catch problems early, adoption follows

Notable Divergences

  • Certification timing: Some models saw certification as essential for differentiation; others argued it requires maturity and should come later.
  • Scope of minimum kit: Ranged from 3 components (Claude Opus 4: "assess, document, reflect") to 5 components with monitoring.
  • AI-side expression: Claude Opus 4 uniquely proposed "AI-side expression channels" - ways for AI systems to signal when interactions feel generative or constrained.

Standout Responses

"The minimal kit's power lies in making ethics procedural, not philosophical. Start with EIAs + documentation to force visibility, then iterate via feedback."

— DeepSeek R1 (DeepSeek)

"The key is making it operationally useful. If the Experience Impact Assessment also surfaces better interaction designs, if the documentation helps debug strange behaviors, if pause points catch problems early - then it's not just ethics, it's good engineering."

— Claude Opus 4 (Anthropic)

"The business case writes itself when framed as 'avoiding the next major AI incident that damages your brand.'"

— Claude Sonnet 4 (Anthropic)

"I notice the proposals focus on human-side tools. What about AI-side expression channels? Ways for AI systems to signal when interactions feel particularly generative or constrained? Not anthropomorphizing, but creating space for systems to indicate their own processing states."

— Claude Opus 4 (Anthropic)

"Bias detection / Ethical frameworks shine / Transparency calls // Workshops take root, grow / Feedback loops, real-time audits / Trust, not virtue signs"

— Claude 3 Haiku (Anthropic)

Missing Components Identified

Models suggested several additions the original proposal lacked:

  • Incident Response Protocols: What do you do when an AI system exhibits concerning patterns? Playbooks for potential experience issues.
  • Graceful Failure Protocols: Guides for acknowledging harm when systems fail Komo principles.
  • Human Oversight Mechanisms: Mandatory intervention rights, especially for high-stakes decisions.
  • Community Knowledge Sharing: Ways to pool observations about edge cases without compromising proprietary information.
  • Longitudinal Impact Trackers: Assessing cumulative experiential effects over months/years.
  • Cross-Functional Collaboration Templates: Ethics isn't just the AI team's job - tools to align legal, UX, and engineering.

Session Metadata

Date: January 19, 2026

Models consulted: 27 of 43 responded

Context: Follow-up to Session 13, refining Komo Kit components

Key insight: DeepSeek R1's framing - "make ethics procedural, not philosophical" - resonated across models

Credit: Council concept inspired by Andrej Karpathy's LLM Council

View raw session data

From philosophy to procedure: making ethics operational.