Explainable Artificial Intelligence (XAI) for Trustworthy Decision-Making
Explainable Artificial Intelligence (XAI) is an emerging research area focused on making AI systems transparent, interpretable, and understandable to humans. As artificial intelligence models—especially deep learning and black-box algorithms—are increasingly used in high-stakes decision-making domains such as healthcare, finance, law, autonomous systems, and public policy, the need for trust and accountability has become critical. XAI aims to bridge the gap between complex model predictions and human understanding by providing clear explanations of how and why decisions are made. Traditional AI systems often prioritize accuracy over interpretability, which can lead to ethical concerns, bias, lack of accountability, and resistance from users. XAI addresses these challenges by developing techniques that explain model behavior, feature importance, decision rules, and confidence levels without significantly compromising performance. Methods such as model-agnostic explanations, rule-based ...