explainability
What It Means
Explainability is your AI system's ability to tell you why it made a specific decision in terms that humans can understand. It's like having a transparent employee who can walk you through their reasoning process step-by-step. When your AI recommends rejecting a loan application or flagging a transaction as fraud, explainability means you can see which factors drove that decision.
Why Chief AI Officers Care
Regulators increasingly require explanations for AI decisions that affect people, especially in finance, healthcare, and hiring where you face legal liability. Without explainability, you can't debug when AI goes wrong, can't build stakeholder trust, and can't meet compliance requirements like fair lending laws. It's also essential for gaining business user adoption since people won't trust black box systems making important decisions.
Real-World Example
A bank's AI denies a mortgage application and explains: 'Decision based on debt-to-income ratio of 47% (threshold is 43%), credit utilization of 78% across 3 cards, and 2 late payments in the past 12 months.' This explanation helps the loan officer understand the decision, allows the customer to address specific issues, and provides documentation for regulatory compliance.
Common Confusion
People often confuse explainability with interpretability - explainability is about getting after-the-fact reasons for decisions, while interpretability is about understanding the overall model behavior. Many also think explainability means dumbing down AI, when it's actually about making sophisticated systems accountable and trustworthy.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare, explainability is critical for clinical decision support systems where physicians need to understand why ...
Finance: In finance, explainability is critical for regulatory compliance with laws like the Fair Credit Reporting Act (FCRA) and...
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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
"The ability to provide a human interpretable explanation for a machine learning prediction and produce insights about the causes of decisions, potentially to line up with human reasoning. "Source: NISTIR_8269_Draft
"Within the context of AI, the extent to which AI decisioning processes and outcomes are reasonably understood."Source: Comptroller_Office
"A characteristic of an AI system in which there is provision of accompanying evidence or reasons for system output in a manner that is meaningful or understandable to individual users (as well as to developers and auditors) and reflects the system’s process for generating the output (e.g., what alternatives were considered, but not proposed, and why not)."Source: NSCAI
Related Terms
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