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説明可能な人工知能(XAI)の基礎概念
基礎スキルレベル
更新日 2026/05
TheoryArtificial Intelligence1時間12 ビデオ36 演習2,050 XP7,892修了証明書
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Introduction To Explainable AI
We delve into Explainable AI (XAI), emphasizing its role in rendering AI systems transparent, interpretable, and trustworthy. We explore AI's capabilities in prediction and content generation, underscoring the necessity for clear decision-making processes. Additionally, we investigate methods to make complex AI models more comprehensible to a wide range of audiences.
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Techniques in Explainable AI
We explore Explainable AI (XAI) techniques, categorizing them into model-specific, model-agnostic, local, and global explanations to clarify AI decision-making. We discuss regression and classification for model-specific insights and introduce SHAP and LIME to interpret black box models. Additionally, we address the complexity of Large Language Models (LLMs), emphasizing the need for transparency in their decision-making processes.
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Implementing and Applying XAI
We explore the transformative impact of XAI in making artificial intelligence more accessible and user-friendly across various sectors. By integrating explainability from the outset, we ensure AI systems are transparent, fostering trust and facilitating a deeper collaboration between humans and machines. Through real-world case studies, we highlight how XAI demystifies complex AI decisions, empowering users with diverse technical backgrounds to leverage AI insights for more informed decision-making.
説明可能な人工知能(XAI)の基礎概念
コース完了 19百万人を超える学習者と共に説明可能な人工知能(XAI)の基礎概念を始めましょう!
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