These tools and metrics are designed to help AI actors develop and use trustworthy AI systems and applications that respect human rights and are fair, transparent, explainable, robust, secure and safe.
Judgment Assurance
As AI increasingly informs consequential decisions, a persistent gap has emerged: the governance of human judgment itself. Judgment Assurance closes this gap by treating judgment not as an individual intuition, but as a deliberate institutional asset.
This tool suite provides a technology-agnostic, scalable, governance layer that complements existing technical AI frameworks. It includes:
The Core Framework: A methodology for defining and preserving "Institutional Inheritance" and judgment maturity.
JA-UQ (Underwriting Questionnaire): An evidence-weighted instrument for evaluating accountability and oversight maturity in audit and regulatory contexts.
JAMM-PS (Maturity Model): A verifiable tiering system (Levels 0–4) that defines the assurance floor for decision reconstructibility.
Designed for high-stakes environments, Judgment Assurance ensures that when AI influences an outcome, the human "why" is captured contemporaneously rather than post-hoc. It provides a common vocabulary for boards, insurers, and regulators to measure decision-governance maturity independently of AI architecture.
Boundary Conditions: Judgment Assurance is a discipline for governing human oversight and does not assess technical model performance, data bias, or algorithmic accuracy. It is designed to complement existing technical AI controls by addressing the specific "Accountability Gap" in human-in-the-loop decision-making.
About the tool
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Tags:
- transparency
- trustworthiness
- auditablility
- accountability
- ai guardrails
- model risk management
- responsible ai
- self-assessment
- AI Governance & Policy
- artificial intelligence governance
- human-in-the-loop ai governance
- ai accountability platform
- ai assurance
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