Explainable Artificial Intelligence Approaches: A Survey

Black box AI systems pose adoption barriers in high-stakes applications: Despite superior performance in many real-world tasks, the lack of explainability in AI decision-making is a "key stumbling block" for deploying these systems in critical domains where understanding decisions is essential for trust and accountability.

Multiple XAI methods exist but each has tradeoffs: The survey identifies that popular Explainable AI approaches have different "merits and demerits," suggesting practitioners must evaluate which method best fits their specific needs rather than assuming one solution works universally.

Explainability can be measured and quantified: The paper addresses "quantifying explainability," indicating that organizations can move beyond subjective assessments and develop concrete metrics to evaluate how well an AI system's decisions can be understood and justified.

Local vs. global explanations serve different purposes: The analysis examines XAI methods from "multiple perspectives (e.g., local, global)," meaning explanations can focus either on individual decisions or broader patterns—each valuable for different stakeholder needs in responsible AI deployment.

XAI is essential infrastructure for responsible AI: The survey positions XAI as "a medium" toward achieving human-centered and responsible AI systems, suggesting that explainability isn't optional but foundational for ethically deploying AI in consequential decisions like credit approval.