Explainable Artificial Intelligence (XAI) Concepts
Offered By: DataCamp
Course Description
Overview
Understand the role and real-world realities of Explainable Artificial Intelligence (XAI) with this beginner friendly course.
This course will dive into the captivating world of Explainable AI (XAI), a discipline that seeks to bring clarity and understanding to the often mysterious realm of artificial intelligence. This conceptual course is crafted with the non-technical audience in mind, aiming to illuminate the importance and methods behind making AI decisions transparent. Participants will delve into the rationale for XAI, its role amidst the broader landscape of AI, and its significant societal implications. By the course end, learners across varied roles will appreciate how XAI bridges the gap between intricate algorithms and everyday decision-making.
This course will dive into the captivating world of Explainable AI (XAI), a discipline that seeks to bring clarity and understanding to the often mysterious realm of artificial intelligence. This conceptual course is crafted with the non-technical audience in mind, aiming to illuminate the importance and methods behind making AI decisions transparent. Participants will delve into the rationale for XAI, its role amidst the broader landscape of AI, and its significant societal implications. By the course end, learners across varied roles will appreciate how XAI bridges the gap between intricate algorithms and everyday decision-making.
Syllabus
- 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.
- 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.
- 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.
Taught by
Folkert Stijnman
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