The Need for Explainable Artificial Intelligence: Opening the Black Box of AI

This sparked the rise of "explainable AI" (XAI), a burgeoning field aiming to shed light on automated outputs without compromising performance.

As artificial intelligence progressively automates high-risk decision-making, demand has grown for insights into opaque algorithms. Commonly referred to as a "black box", today's sophisticated AI systems process vast amounts of data but their internal reasoning remains elusive.

This sparked the rise of "explainable AI" (XAI), a burgeoning field aiming to shed light on automated outputs without compromising performance. Pioneering new techniques, XAI researchers are developing approaches to interpret machine learning models by assessing feature importance, generating natural language explanations, or identifying representative examples.

While progress has been significant, the solution requires going beyond technical explanations alone. Providing comprehendible justifications for stakeholders across domains demands evolving solutions into broadly "understandable AI".

Need for explainability in AI

The demand for transparency in AI is growing. As machines make more complex decisions that imp…