AI Correctness

A fundamental challenge in trust calibration: How do you know if the AI was correct?

The Challenge

Traditional approaches require ground truth labels — waiting for outcomes. But:

• Outcomes may take months (loan default, treatment success)

• Some outcomes are never observed (rejected applicants)

• Counterfactuals are unobservable (what would have happened if...)

ATHENA's Solution

Patent-Pending Technology (USPTO-001)

ATHENA uses proprietary counterfactual analysis to determine AI correctness in real-time, without requiring ground truth labels or waiting for outcomes.

When User Follows AI

When the user follows the AI recommendation, the eventual outcome (if observed) determines correctness.

{
  "ai_recommendation": "Approve loan",
  "user_decision": "Approve loan",
  "outcome": "default",
  "ai_correct": false
}

When User Overrides AI

When the user overrides the AI, ATHENA's patent-pending algorithms estimate what would have happened:

{
  "ai_recommendation": "Approve loan",
  "user_decision": "Deny loan",
  "outcome": null,
  "ai_correct": "estimated_false",
  "confidence": 0.87
}

What Makes This Novel

Our patent-pending approach is the first system capable of:

1. Determining AI correctness without ground truth labels

2. Real-time estimation (sub-100ms response)

3. Domain-agnostic operation across healthcare, finance, legal, and more

4. Validated accuracy across 390M+ decision records

Validation

ATHENA's methodology has been validated across:

Dataset	Records	Industries
Combined validation	390M+	14 industries

Validation includes healthcare, finance, legal, insurance, and other high-stakes domains where trust calibration is critical.

API Example

const result = await athena.calibrate.analyze({
  userId: 'user_123',
  aiRecommendation: 'Approve loan',
  userDecision: 'Deny loan',
  outcome: null,
  context: {
    // Your domain-specific context
  }
});

console.log(result.aiCorrect);    // false (estimated)
console.log(result.confidence);   // 0.87

Confidence Levels

ATHENA provides confidence levels for correctness estimates:

Confidence	Meaning	Use Case
>0.9	High confidence	Use in compliance reports
0.7-0.9	Moderate confidence	Flag for human review
<0.7	Low confidence	Exclude from analysis

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Next: Bias Detection