TruthCalibrate

Codename: The Uncertainty Engine Version: 1.0 Status: Draft Strategic Alignment: Phase 2 (Trust & Governance) of the Machine-First Maturity Model.

1. Executive Summary

The Core Problem: Human marketing relies on persuasion and absolutes ("Guaranteed results," "Best in class"). However, AI reasoning engines view absolute confidence as a hallucination risk. An AI that never expresses uncertainty is dangerous. Consequently, AI intermediaries (like Amazon Rufus or ChatGPT) often discard marketing data because it lacks nuance, preferring to cite neutral third parties instead. The Solution: TruthCalibrate is a Confidence Calibration Middleware. It intercepts data claims and appends explicit Confidence Intervals, Validity Windows, and Contextual Bounds. It transforms absolute assertions into probabilistic truths. The Goal: To engineer "Epistemic Honesty". By admitting limits, we paradoxically increase the machine's trust in our data, making our brand the "safe choice" for an AI to recommend.

2. User Personas

• The Risk/Compliance Officer (Primary): Needs to ensure that the AI never presents a probabilistic inference (e.g., "likely to approve loan") as a guaranteed fact to a customer.

• The Data Engineer: Needs a standardized schema to encode "validity" into API responses without breaking legacy systems.

• The AI Strategist: Wants to increase the brand's "inclusion in judgment" by providing the structured evidence that machines crave.

3. Core Value Proposition

• Trust Through Limitation: "Paradoxically, admitting limits increases trust." A system that says "this depends" is easier for an AI to reason with than one that presents absolutes.

• Safety as Code: Prevents "overcommitment" by ensuring the machine knows when not to act.

• Predictable Correctness: Moves the brand from competing on "experience" (persuasion) to competing on "judgment" (evidence).

4. Functional Requirements

4.1. The Confidence Appender

• Requirement: The system must attach a confidence score (float 0.0 to 1.0 or qualitative High/Medium/Low) to every inference or claim processed.

• Rationale: "Explicit uncertainty is a feature, not a bug". Machines need to calculate risk before recommending an action.

• Input/Output:

    ◦ Input: "Product removes stains."

    ◦ Output: {"claim": "Product removes stains", "confidence": 0.85, "basis": "internal_testing"}.

4.2. Validity Window Management

• Requirement: Every data object must support Validity Windows (start date, end date, or "freshness" TTL).

• Rationale: Knowledge changes. Rules evolve. Information that is true today may be false tomorrow. Systems without validity windows degrade silently.

• Key Feature: Automatic Expiry. The API must stop serving a claim if its validity window has passed, preventing the hallucination of outdated facts (e.g., an old interest rate or price).

4.3. Contextual Bounding (Jurisdiction & Condition)

• Requirement: The system must allow users to define Context Limits (e.g., "Valid only in EU," "Valid only for oily skin").

• Rationale: A claim is rarely universally true. "Universal" claims are flagged as high-risk by reasoning engines. Bounded claims are treated as evidence.

• Key Feature: Jurisdiction Enforcer. If an API query originates from outside the valid context, TruthCalibrate must return a "Refusal" or "Conditional" response.

4.4. The "Hedge" Translator

• Requirement: A natural language processing module that converts marketing absolutes into machine-legible accuracy mechanisms.

• Rationale: Translating "Usually" and "Depends on" from vague linguistic hedges into structural accuracy mechanisms.

• Example: Converts "Instant delivery" → delivery_estimate: { val: "immediate", probability: 0.9, condition: "network_availability" }.

5. Technical Architecture & Schema

TruthCalibrate wraps existing API endpoints to transform "Objects" into "Reasoning Surfaces".

Target JSON Schema:

{
  "entity_id": "policy_123",
  "attribute": "refund_eligibility",
  "value": "true",
  "meta_calibration": {
    "confidence_score": 0.95,
    "validity_window": {
      "start": "2024-01-01T00:00:00Z",
      "end": "2024-12-31T23:59:59Z"
    },
    "constraints": [
      {
        "type": "jurisdiction",
        "allow": ["US", "CA"],
        "refuse": ["EU"]
      },
      {
        "type": "context",
        "condition": "item_unopened"
      }
    ],
    "source_type": "contractual_obligation" // Layer 1 (Authority) [11]
  }
}

6. Use Cases

Use Case A: The Financial Advisor AI (Banking)

• Problem: An AI assistant tells a user, "You will be approved for this mortgage." The user is rejected and sues.

• TruthCalibrate Solution: The system appends a 0.7 confidence score to the approval prediction based on credit score volatility.

• Outcome: The AI reads the score and changes its output to: "Based on current data, approval is likely (70%), provided your debt ratio remains stable." The limit is admitted, safety is preserved.

Use Case B: The Skincare Recommendation (E-Commerce)

• Problem: Marketing copy says a cream "Cures Acne." Amazon's Rufus AI flags this as a medical hallucination risk and suppresses the product.

• TruthCalibrate Solution: The system bounds the claim: {"claim": "Reduces Acne", "confidence": 0.8, "context": "mild to moderate cases"}.

• Outcome: The AI cites the product as a viable option for specific users because the claim is "defensible" and "structured".

7. Success Metrics (KPIs)

1. Citation Frequency: Increase in the brand's data being cited as "Evidence" rather than ignored.

2. Refusal Rate: The number of times the system correctly refuses to answer or act because confidence was below the safe threshold (Target: >0% implies the system is working).

3. Correction Lag: Time taken to expire outdated information (Target: <100ms via Validity Windows).

8. Roadmap

• Phase 1 (The Bounding Box): Manual entry of validity windows and confidence scores for top 100 "Authoritative Knowledge" assets.

• Phase 2 (The Automated Tagger): NLP integration to automatically assign confidence scores based on source authority (e.g., Lab Report = 1.0, User Review = 0.5).

• Phase 3 (The Circuit Breaker): Integration with "SafeState" to trigger automatic kill switches if data confidence drops below 0.5.

AI Visibility ToolsFrancesca Tabor