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説明可能な人工知能（XAI）の基礎概念

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更新日 2026/05

初心者向けコースで、Explainable Artificial Intelligence (XAI) の役割と実務での実態を理解しましょう。

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TheoryArtificial Intelligence

1時間

12 ビデオ

36 演習

2,050 XP

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コース説明

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1

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.

Why Explainable AI matters

Transparency or interpretability

Interpretable scenarios

XAI objectives

The technicalities of XAI

Understanding the fundamentals of XAI

Accuracy and interpretability

Communicating about XAI

Audience segmentation

Balancing accuracy and simplicity

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2

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.

XAI techniques

Local or global

Model-specific or model-agnostic

Model-specific explanations

Analogies for regression and classification

Black box models in decision making

Model-agnostic explanations

SHAP, LIME or Both?

Role of SHAP and LIME in XAI

Explainability of LLMs

Understanding LLMs

Reasoning of LLMs

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3

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.

Tailoring AI to the user

XAI for the end-user

Risks of not including the end-user

XAI by design

Job applicant screening

Applying XAI by design

XAI for online learning

XAI in action

XAI requirements per industry

Trusting the AI system

Future of XAI

Cross disciplinary XAI

Proactive development and deployment

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説明可能な人工知能（XAI）の基礎概念

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