📋 AI Model Explainability Requirement Simulator

The AI Model Produces a Clinical Output

Placeholder: patient features flow into a trained model.

• 6: Input features used (placeholder feature set)
• Risk Score: Output type (placeholder output form)
• Adjustable: Model class (placeholder complexity range)
• <100ms: Inference time (placeholder latency figure)

What the model sees

Placeholder text describing feature ingestion, short.

From features to score

Placeholder text describing forward pass, short summary.

Black-Box Scenario — Output With No Reasoning

Placeholder: the score arrives with no supporting detail.

• None: Explanation provided (placeholder black-box state)
• Low: Clinician confidence (placeholder trust level)
• Limited: Error detection ability (placeholder review capacity)
• Reduced: Regulatory acceptance (placeholder compliance note)

Why opacity is risky

Placeholder text on black-box clinical risk, short.

Trust without evidence

Placeholder text on unverifiable outputs, brief note.

Explainable-AI Techniques Reveal Contributing Factors

Placeholder: attribution method scores each input feature.

• Feature Attribution: Method type (placeholder method name)
• Up to 6: Features ranked (placeholder ranked count)
• Bar breakdown: Output form (placeholder visualization type)
• Minor: Added latency (placeholder compute cost)

How attribution works

Placeholder text on scoring feature influence, short.

Reading the bars

Placeholder text on bar magnitude meaning, brief.

Clinician Sanity-Checks the Explanation

Placeholder: clinician compares reasoning to clinical knowledge.

• Plausibility Check: Review step (placeholder review type)
• Variable: Flag rate (placeholder flag frequency)
• Advisory: Decision support role (placeholder role note)
• Retained: Override capability (placeholder clinician authority)

Sanity-checking reasoning

Placeholder text on plausibility review, short summary.

Catching model errors

Placeholder text on error-catching benefit, brief.

Trust Improves, but Complexity Costs Explainability

Placeholder: complex models trade clarity for raw accuracy.

• Positive: Trust gain (placeholder outcome direction)
• Improved: Error catch rate (placeholder benefit metric)
• Inherent Tradeoff: Accuracy vs. clarity (placeholder tension note)
• Harder to Explain: High-complexity models (placeholder deep-model note)

The inherent tradeoff

Placeholder text on complexity vs explainability tension.

Placeholder: deep models gain accuracy, lose clean explainability.

Balancing the two

Placeholder text on choosing model class by context.