Interpretability

Interpretability has multiple competing meanings in Artificial Intelligence and Machine Learning: Interpretability (Research Usage)—understanding model behavior and mechanisms; and Interpretability (NIST Usage)—understanding outputs in functional context. In common research usage, simple Linear Regression models and small Decision Trees are often more interpretable than complex Deep Learning systems, including Large Language Models. The NIST AI Risk Management Framework distinguishes contextual interpretability from Explainability, which concerns representations of the mechanisms underlying a system’s operation. Other literature uses these terms interchangeably or reverses the distinction; explainability is not restricted to Post-hoc Explanations, and explanations should faithfully reflect the system’s processes rather than merely justify outputs. These capabilities can support AI Ethics, debugging, risk management, accountability, and compliance, particularly in high-impact settings such as healthcare and finance, but regulatory obligations depend on applicable laws, standards, risk classifications, and context. Trustworthy AI is broader, also encompassing validity, reliability, safety, security, resilience, transparency, privacy, and fairness with harmful bias managed. Google DeepMind and OpenAI research interpretability to better understand model behavior, investigate risks including Algorithmic Bias, and support AI Alignment; interpretability alone does not ensure alignment with human intent.