COMPEL Glossary / interpretability
Interpretability
Interpretability is the degree to which a human can understand the internal mechanisms and decision-making logic of an AI model, enabling meaningful inspection of how inputs are transformed into outputs.
What this means in practice
Highly interpretable models like decision trees and linear regression allow direct examination of decision rules, while complex models like deep neural networks require post-hoc interpretation techniques that approximate rather than reveal the actual decision process. For organizations, interpretability affects governance capability because systems that cannot be understood cannot be effectively audited, debugged, or improved. In COMPEL, interpretability requirements are calibrated based on risk level and regulatory context during the governance architecture design in Module 3.4, with higher-risk applications requiring higher interpretability to enable meaningful human oversight.
Why it matters
Systems that cannot be understood cannot be effectively audited, debugged, or improved. Interpretability determines an organization's governance capability over its AI systems. Regulatory frameworks increasingly require interpretability for high-risk applications, and affected individuals have growing rights to meaningful explanations. Organizations deploying opaque models in consequential domains face both compliance risk and operational difficulty in diagnosing and fixing problems.
How COMPEL uses it
During Calibrate, interpretability requirements are mapped based on use case risk levels and regulatory context. The Model stage calibrates interpretability requirements within the governance architecture design in Module 3.4, with higher-risk applications requiring higher interpretability. The Produce stage implements appropriate interpretation techniques. The Evaluate stage assesses whether interpretability levels are sufficient for meaningful human oversight.
Related Terms
Other glossary terms mentioned in this entry's definition and context.