<p>❶ The article emphasizes the importance of transparency in AI decision-making, particularly in fields like medical diagnostics and recruitment, where understanding the rationale behind AI outputs is critical for trust and model improvement.</p><p>❷ It highlights two main focuses of Explainable AI (XAI): enhancing data/model quality for engineers and addressing ethical requirements to provide user-centric explanations, ensuring responsible AI deployment.</p><p>❸ The whitepaper advocates advancing XAI research, standardizing tools for large-scale models, integrating XAI into AI education, and encouraging corporate adoption to foster collaboration between human expertise and machine learning.</p>