explainability

Explainability refers to the degree to which the outputs and decision-making processes of complex systems, particularly artificial intelligence (AI) and machine learning models, can be understood and interpreted by humans. It is a critical aspect of trustworthy AI, enabling users, developers, and stakeholders to comprehend how and why specific outcomes are produced. Explainability facilitates transparency, accountability, and debugging, especially when models are deployed in sensitive areas such as healthcare, finance, and autonomous systems.

There are two primary types of explainability: global and local. Global explainability pertains to understanding the overall behavior of a model, including its structure and underlying rules. Local explainability focuses on clarifying individual predictions or decisions, providing insights into why a specific prediction was made. Techniques to enhance explainability vary, ranging from simple models like decision trees and linear regressions, which are inherently interpretable, to complex models like neural networks, which require post-hoc explanation methods.

Methods such as feature importance analysis, saliency maps, decision rules, and surrogate models are commonly used to provide explanations for complex models. The choice of approach depends on the context, the need for interpretability, and the complexity of the system.

Explainability is increasingly viewed as essential for compliance with ethical standards and regulations, such as the European Union’s General Data Protection Regulation (GDPR), which emphasizes the right to explanation. It also supports better user trust, facilitates model validation, and helps identify biases or errors within AI systems. As AI technologies evolve, the pursuit of effective and scalable explainability remains a vital area of research and development.