AI Evaluation Pipeline - Citizens

🚀 Episode 2: What Data Citizens & SMEs Need to Know About AI Evaluation

If an AI model scores 98% accuracy in a test environment but is built on quicksand data, it doesn't create enterprise value, it creates a data cascade failure.

In Episode 2 of the Cradle-to-Grave AI Evaluation Pipeline (LC-02 & LC-05: Build & Supply Certification), the focus shifts from upper-spine strategy to upstream data manufacturing and semantic truth.

Here is how Data Citizens, Business Subject Matter Experts (SMEs), and Human-in-the-Loop (HITL) specialists define "what is good" for an enterprise AI system before model training or fine-tuning ever begins:

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🔍 1. Moving Beyond Soft Data Checks to Programmatic Contracts
Data Citizens and Data Stewards don't rely on generic data cleanliness promises. Instead, they enforce Artifact 2: The Data Product Evaluation Contract. Every input dataset must originate from a certified Data Product (e.g., Gold or Silver badge status) with an assigned domain owner.

📐 2. Applying Dan Myers' CDDQ Framework to Critical Data Elements (CDEs)
SMEs isolate high-consequence attribute, Critical Data Elements (CDEs), that carry legal or operational decision weight. Using Dan Myers' Conformed Dimensions of Data Quality (CDDQ), they write explicit, programmatic assertion rules with zero-tolerance thresholds:

• Uniqueness & Validity: 0% nulls / 0% duplicates
• Semantic Range Accuracy: valid monetary or numerical bounds
• Freshness & Staleness: e.g., maximum staleness < 7 days
• Domain Conformance: zero unknown or unmapped codes

🧠 3. Establishing Semantic Grounding & Hallucination Boundaries
To prevent Generative AI and LLMs from inventing domain logic, SMEs and business architects formally bind model inputs and outputs to governed enterprise business dictionary terms, known as Information Concepts (IC).

• Hallucination Boundaries: Models are explicitly prohibited from making ungrounded claims without citing verified baseline sources or control groups.
• Disambiguation: The pipeline automatically rejects conflicting synonyms that violate the enterprise data dictionary (e.g., confusing an 'Output' with an 'Outcome').

👥 4. Setting HITL Escalation Rules & Precision Gates
Subject Matter Experts and HITL practitioners define the operational guardrails governing human oversight:

• Decision Rules (DMN): If model confidence drops below a threshold (e.g., < 0.85) or data quality is degraded (e.g., < 0.90), the decision automatically escalates to a human specialist panel.
• HITL Precision Gate: Human oversight is benchmarked to ensure escalation precision meets strict operational standards (e.g., > 0.90).

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🛑 The Business Owner's Hard Call: "HALT THE BUILD"
Episode 2 arms business leaders and data domain owners with a non-negotiable operational verdict:
If upstream data products lack certified contracts, fail CDE assertion rules, or miss semantic concept bindings, the business owner executes a "HALT THE BUILD" decision. Pipeline execution is frozen immediately, preventing costly AI models from being built on faulty foundations.

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💬 How does your organization certify data supplies before training AI models? Are your SMEs setting programmatic hallucination boundaries? Let's discuss!

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