Model Transparency and Interpretability

Model transparency and interpretability are central themes in the governance of machine learning (ML) systems. These concepts refer to the ability to understand and explain how machine learning models make decisions and are crucial for ensuring that ML systems operate in an ethical and accountable manner. Transparency involves providing insight into the inner workings and processes of ML models, while interpretability refers to the degree to which a human can comprehend the rationale behind a model’s predictions or decisions.