May 1, 2024
Updated June 26, 2025
25 minute read
Unveiling the World of Explainable AI: Understanding the "How" and "Why" Behind Artificial Intelligence
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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.
Cornerstone for anyone diving into Explainable AI. It provides a comprehensive overview of methods and tools for interpreting machine learning models, particularly focusing on model-agnostic techniques. It's highly valuable as a primary reference for practitioners and researchers. The book is commonly used in both academic and industry settings due to its practical approach and clear explanations.
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.
Offers a practical, code-centric approach to interpretable machine learning using Python. It provides hands-on examples for building explainable, fair, and robust models. This valuable resource for practitioners and students who want to implement XAI techniques.
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.
Provides a hands-on approach to XAI using Python, covering various techniques and their implementation. It's geared towards practitioners and includes practical examples. It helps solidify understanding through practical application and good complement to more theoretical texts.
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.
Is part of a series on the ethics of AI and provides a collection of essays on various ethical considerations surrounding artificial intelligence. It includes discussions on topics relevant to XAI, such as bias, fairness, and accountability. It's a good resource for exploring the ethical landscape that necessitates explainable systems.
This influential book highlights the potential negative societal impacts of opaque algorithms, particularly in areas like hiring, credit, and criminal justice. It serves as a compelling argument for the importance of explainability and fairness in AI systems, making the case for why XAI crucial field.
Examines how algorithms can perpetuate and reinforce social inequalities, particularly focusing on race and gender in search engine results. It provides critical insights into the real-world harms of biased algorithms and underscores the need for transparent and explainable AI systems.
Provides a philosophical introduction to the ethical issues surrounding artificial intelligence. It covers a range of topics including responsibility, accountability, and the impact of AI on society. It's a valuable resource for gaining a foundational understanding of the ethical context of XAI.
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.
This foundational textbook for deep learning, a powerful class of models that are often considered 'black boxes.' While it doesn't focus on XAI techniques, a solid understanding of deep learning architectures is necessary to apply and develop explainability methods for them. It's a core text for graduate students and researchers in AI.
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.
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.
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.
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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