Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction

Black-box AI poses real risks in critical domains: The paper identifies that AI models' inability to explain their decisions is a significant problem specifically in safety-critical areas like healthcare, finance, and autonomous vehicles—domains where understanding why a decision was made can directly affect human welfare and safety.

Explainable AI serves multiple stakeholders: XAI benefits aren't limited to developers; the paper emphasizes that various groups—including end-users, regulators, and affected individuals—are "beneficiaries of XAI," meaning transparency requirements should address the needs of diverse audiences, not just technical teams.

Transparency and accountability are interconnected: The survey highlights that XAI addresses three core responsible AI concerns simultaneously: transparency (understanding how models work), accountability (being able to justify decisions), and fairness (ensuring decisions don't discriminate)—suggesting these goals reinforce rather than compete with each other.

There's no one-size-fits-all XAI solution: The paper presents a "taxonomy of XAI methods," indicating that different explanation techniques exist for different situations, implying that organizations need to thoughtfully select appropriate XAI approaches based on their specific application and stakeholder needs rather than applying generic solutions.

XAI is becoming essential practice, not optional: By framing XAI as addressing fundamental challenges to AI trustworthiness across multiple sectors, the survey positions explainability as a core requirement for responsible AI deployment rather than an add-on feature.