artifacts/intake-archive/20260622__continuity-office-intake/012-explainable-failure-for-ai-systems

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--- catalog: "Free Training Catalog" training_id: "012" title: "Explainable Failure for AI Systems" subtitle: "Making AI mistakes survivable" track: "AI & Automation Continuity" estimated_time: "20–30 minutes" audience:

• IT / Security
• Product
• Compliance
• AI teams

learning_outcomes:

• Distinguish explainable vs opaque AI failure
• Design AI systems with recoverable failure modes
• Preserve accountability under automation

prerequisites: "Training 001–011 recommended" level: "Intermediate" license: "Free / Open Training" version: "1.0" last_updated: "2025-12-18" ---

Explainable Failure for AI Systems

Making AI mistakes survivable

Core stance

AI will fail. The question is whether humans can explain, defend, and correct those failures.

Explainable vs opaque failure

Explainable failure:

• Can be described in human language
• Has a traceable input or assumption
• Supports correction

Opaque failure:

• “The model just did that”
• No clear accountability
• No learning retained

Designing for explainable failure

• Log inputs and decision context
• Mark confidence and uncertainty
• Define escalation paths
• Preserve human override

Exercises

• Identify one AI output you could not defend today
• Add one uncertainty or confidence marker
• Define who may stop the system

Suggested next step

Require an explanation pathway before scaling any AI system.