Explaining Explanations: An Overview of Interpretability of Machine Learning

Explanations of AI decisions are currently unstandardized and lack systematic assessment, meaning there's no consistent way to evaluate whether an AI system's explanation is actually reliable or trustworthy—a critical gap for responsible deployment.

Understanding how AI systems make decisions is essential for detecting bias and fairness problems that might otherwise go unnoticed, making explainability a key tool for identifying when algorithms discriminate or rely on flawed training data.

Current explanation methods, particularly for deep neural networks, are fundamentally insufficient for the level of transparency needed, suggesting that many popular AI systems today cannot adequately explain their reasoning to users or regulators.

Explainability serves multiple stakeholder needs—it helps users understand decisions affecting them, allows internal system components to communicate with each other, and enables verification that algorithms perform as intended, making it essential across different levels of AI governance.

The field lacks established best practices and faces significant open challenges, indicating that responsible AI deployment requires continued research into how we define, measure, and improve explanations before these systems can be safely integrated into high-stakes domains like healthcare, criminal justice, or lending.