interpretability
What It Means
Interpretability is your ability to understand why an AI system made a specific decision or prediction. It means you can trace the reasoning from input data through to the final output, and predict how the system will behave when conditions change. Think of it as being able to 'look under the hood' and explain the AI's decision-making process in plain English.
Why Chief AI Officers Care
Interpretability is critical for regulatory compliance, especially in finance, healthcare, and hiring where you must justify AI decisions to auditors and regulators. It directly impacts trust and adoption - business users won't rely on AI recommendations they can't understand or verify. Without interpretability, you're also blind to potential bias, errors, or system failures that could create legal liability or business risk.
Real-World Example
A bank's AI system rejects a loan application, and the loan officer needs to explain the decision to the customer and document it for compliance. An interpretable system would show that the rejection was based on debt-to-income ratio (40%) and recent credit inquiries (30%), while an uninterpretable 'black box' system would only output 'denied' with no explanation, creating regulatory problems and frustrated customers.
Common Confusion
People often confuse interpretability with accuracy - assuming that more complex, less interpretable models are always better performers. In reality, simpler, more interpretable models often perform just as well and provide much more business value through trust and compliance benefits.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare AI, interpretability is critical for clinical trust and patient safety, as physicians need to understand w...
Finance: In finance, interpretability is critical for regulatory compliance and risk management, as institutions must explain AI-...
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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 understand the value and accuracy of system output. Interpretability refers to the extent to which a cause and effect can be observed within a system or to which what is going to happen given a change in input or algorithmic parameters can be predicted. "Source: NSCAI
"The ability to explain or to present an ML model’s reasoning in understandable terms to a human"Source: aime_measurement_2022, citing Machine Learning Glossary by Google
Related Terms
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