Reviewing the Need for Explainable Artificial Intelligence (xAI)

The "black box" problem remains inadequately solved: Despite rapid expansion of explainable AI research, the field has not yet developed a comprehensive solution to making AI decision-making transparent. The paper identifies this as a central unresolved challenge, suggesting current xAI methods provide only partial answers to why AI systems make specific decisions.

Multiple competing approaches create confusion about what "explainability" actually means: The paper identifies four thematic debates within xAI scholarship, indicating the field lacks consensus on fundamental definitions. This fragmentation means organizations and policymakers cannot easily determine which explanation methods best serve their needs for accountability and transparency.

Current xAI methods focus heavily on technical extraction rather than human understanding: The paper notes that xAI research emphasizes "extracting information and visualizing the output" of AI systems, but this technical focus may not translate into explanations that non-experts can actually understand and act upon—a critical gap for responsible AI deployment.

A coordinated research agenda is needed to advance the field: The authors call for synthesized findings into a "future research agenda," suggesting that xAI scholarship has grown somewhat haphazardly. More coordinated effort is required to ensure xAI research directly addresses real-world needs for explainable AI in organizations and society.