Explainable AI: current status and future directions

Explainability is critical for high-stakes decisions: The paper identifies defense, healthcare, law and order, and autonomous vehicles as domains where understanding how an AI system reached its conclusion is essential for building trust and ensuring transparency—not just knowing what decision was made.

Traditional AI systems cannot explain their reasoning: Unlike explainable AI approaches, conventional AI systems cannot answer "why" questions about their outputs, making it difficult for users and stakeholders to verify whether decisions are fair, accurate, or based on appropriate factors.

Multiple XAI techniques exist but have trade-offs: The paper reviews various explainability techniques across different media types (text, images, audio, and video), noting that each approach has both advantages and shortcomings—suggesting there is no one-size-fits-all solution for AI transparency.

The field is still emerging with room for development: By characterizing XAI as an "emerging area of research," the paper indicates that explainability methods are still being developed and refined, implying that current techniques may be incomplete and future improvements are needed.

Explainability enables accountability: The emphasis on explainability for "critical applications" underscores that being able to explain AI decisions is foundational to holding AI systems and their developers accountable for outcomes that affect people's lives.