May 1, 2024
Updated June 26, 2025
25 minute read
Unveiling the World of Explainable AI: Understanding the "How" and "Why" Behind Artificial Intelligence
Artificial Intelligence (AI) and its subfield, Machine Learning (ML), are rapidly transforming our world, making decisions that affect our daily lives, from loan applications and medical diagnoses to the content we see online. At a high level, AI refers to the capability of computer systems to perform tasks that typically require human intelligence, such as learning, problem-solving, and decision-making. Machine Learning is a specific approach within AI where systems learn from data to identify patterns and make predictions without being explicitly programmed for each task.
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Find a path to becoming a Explainable AI. Learn more at:
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Reading list
We've selected 22 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Explainable AI.
Bridges the gap between theoretical XAI concepts and practical implementation. It focuses on effective and commonly used techniques for explaining model predictions with real-world examples and code in Python. This book is particularly useful for working professionals and advanced students looking to integrate XAI into their machine learning workflows.
Provides a deep dive into the concepts of fairness in machine learning, a critical aspect intertwined with explainability. It explores the limitations and opportunities in building fair algorithms. This is an excellent resource for those looking to deepen their understanding of the ethical dimensions that XAI aims to address.
While not solely focused on XAI, this book is essential for understanding the broader ethical implications of AI, which key driver for explainability. It delves into the science behind designing algorithms that are socially aware, covering fairness, privacy, and transparency. It provides crucial context for why XAI is necessary and is valuable for all audience levels interested in the societal impact of AI.
Offers a critical perspective on AI, examining its societal and environmental costs and the power structures it reinforces. It highlights the hidden labor, data extraction, and environmental impact of AI systems, providing a crucial broader context for the importance of transparency and accountability, which XAI contributes to.
Offers a high-level perspective on the future of AI and the crucial need for alignment with human values. It discusses the problem of control and the importance of building trustworthy AI systems, providing a strong motivation for the field of XAI. It's accessible to a broad audience and offers valuable context for the importance of explainability.
A more accessible introduction to causal inference compared to Pearl's 'Causality.' explains the concepts of causality and its importance in understanding data and making informed decisions. It provides valuable foundational knowledge for understanding how causality relates to building explainable AI systems.
While a more theoretical and foundational text, this book classic in the field of causal inference, which is increasingly relevant to XAI. Understanding causality is crucial for developing truly explanatory AI systems. is best suited for graduate students and researchers seeking a deep theoretical understanding.
Provides a comprehensive overview of XAI, covering both the theoretical foundations and the practical applications.
This comprehensive textbook covers the fundamentals of probabilistic graphical models, which are a powerful tool for modeling complex systems and understanding relationships between variables. This knowledge can be beneficial for developing certain types of interpretable models. It valuable reference for graduate students and researchers.
Another foundational textbook covering the principles of pattern recognition and machine learning from a Bayesian perspective. Similar to 'The Elements of Statistical Learning,' it provides essential background knowledge for understanding machine learning models and their behavior, which prerequisite for effective explainability.
This handbook offers a broad overview of the field of artificial intelligence, with sections dedicated to ethical considerations and the philosophy of AI. It provides a wider academic context for XAI and its place within the broader AI landscape. It can be a useful reference for both students and researchers.
A foundational text in the fields of statistics and machine learning. While not specifically about XAI, it covers essential concepts and techniques that underpin many machine learning models. A strong understanding of these fundamentals is beneficial for comprehending how models work and, subsequently, how to explain them. It's a widely used textbook in academic programs.
Provides a business-oriented introduction to data science and data mining concepts. Understanding the business context and the goals of data-driven projects is important for framing explainability requirements and communicating explanations effectively to stakeholders. It's suitable for undergraduate students and professionals.
Provides a broader perspective on data science in a business context, which can help in understanding the practical applications and the need for explainable models in industry. While not a deep dive into XAI techniques, it offers valuable context for working professionals on how XAI fits into a data-driven strategy.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/ruebwu/explainable
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