explanation
What It Means
AI systems must provide clear reasoning for their decisions and outputs, not just final answers. This means when an AI makes a recommendation or classification, it shows the key factors, data points, or logic steps that led to that conclusion. Think of it as requiring AI to 'show its work' like a student solving a math problem.
Why Chief AI Officers Care
Explanations are critical for regulatory compliance, especially in finance, healthcare, and hiring where decisions must be auditable. They enable business users to trust and validate AI outputs, reducing liability when systems make mistakes. Without explanations, organizations cannot effectively debug issues, improve model performance, or meet growing transparency requirements from regulators and customers.
Real-World Example
A credit approval AI denies a loan application but provides an explanation showing the three main factors: debt-to-income ratio of 65% (threshold is 40%), recent missed payment in credit history, and insufficient employment history of 8 months. This allows the loan officer to discuss specific improvement areas with the applicant and documents the decision for regulatory audits.
Common Confusion
People often confuse explanations with accuracy, assuming explainable AI is automatically more correct. An AI can provide detailed, logical explanations for wrong decisions if trained on biased data or using flawed reasoning patterns.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare AI, explanation is critical for clinical decision support systems, diagnostic tools, and treatment recomme...
Finance: In finance, AI explanation is critical for regulatory compliance and risk management, as models used for credit decision...
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Includes:
- 6 industry-specific applications
- Relevant regulations by sector
- Real compliance scenarios
- Implementation guidance
Technical Definitions
NISTNational Institute of Standards and Technology
"Systems deliver accompanying evidence or reason(s) for all outputs."Source: NISTIR_8269_Draft
"The explanation principle obligates AI systems to supply evidence, support, or reasoning for each output."Source: NISTIR_8312
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