The paper "XAI-Explainable Artificial Intelligence" by Gunning et al. (2019) discusses the importance of explainable artificial intelligence (XAI) in making AI systems more understandable and trustworthy to users. It highlights the need for explanations in critical applications such as defense, medicine, finance, and law, where users must understand, trust, and manage AI systems. The paper reviews various machine learning (ML) techniques, noting that while some models like decision trees are more interpretable, they often have lower accuracy, while others like deep learning are less explainable. The paper discusses the trade-off between ML performance and explainability, emphasizing the need for XAI systems that can provide explanations that are both accurate and understandable. XAI aims to make AI systems more intelligible to humans by providing explanations that help users understand the AI's capabilities, actions, and future behavior. However, explanations are context-dependent and vary based on the user's task, abilities, and expectations. The paper outlines different types of explanations, including full and partial explanations, and discusses the importance of interpretability constraints in models. The paper also addresses user expectations from XAI, noting that different user groups may require different types of explanations. It discusses the challenges of evaluating and measuring the effectiveness of explanations, emphasizing the need for both subjective and objective measures. The paper highlights several challenges in XAI, including the balance between accuracy and interpretability, the use of abstractions to simplify explanations, and the distinction between explaining competencies and decisions. The paper concludes that XAI has the potential to play a significant role in future social and collaborative applications, including knowledge coordination and teaching.The paper "XAI-Explainable Artificial Intelligence" by Gunning et al. (2019) discusses the importance of explainable artificial intelligence (XAI) in making AI systems more understandable and trustworthy to users. It highlights the need for explanations in critical applications such as defense, medicine, finance, and law, where users must understand, trust, and manage AI systems. The paper reviews various machine learning (ML) techniques, noting that while some models like decision trees are more interpretable, they often have lower accuracy, while others like deep learning are less explainable. The paper discusses the trade-off between ML performance and explainability, emphasizing the need for XAI systems that can provide explanations that are both accurate and understandable. XAI aims to make AI systems more intelligible to humans by providing explanations that help users understand the AI's capabilities, actions, and future behavior. However, explanations are context-dependent and vary based on the user's task, abilities, and expectations. The paper outlines different types of explanations, including full and partial explanations, and discusses the importance of interpretability constraints in models. The paper also addresses user expectations from XAI, noting that different user groups may require different types of explanations. It discusses the challenges of evaluating and measuring the effectiveness of explanations, emphasizing the need for both subjective and objective measures. The paper highlights several challenges in XAI, including the balance between accuracy and interpretability, the use of abstractions to simplify explanations, and the distinction between explaining competencies and decisions. The paper concludes that XAI has the potential to play a significant role in future social and collaborative applications, including knowledge coordination and teaching.