Interpretability

Interpretability in AI refers to the ability of humans to understand and make sense of the decision-making process of an AI system, particularly in complex or high-stakes applications. 

It involves providing insights into how the system works, how it arrived at a particular decision, and what factors were considered in that decision. Interpretability is crucial for building trust and confidence in AI systems, as well as for ensuring accountability, fairness, and ethical use of the technology. 

Techniques for improving interpretability include visualization, feature attribution, and model distillation, among others.