Explainable Ai With Python
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Explainable Ai With Python
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Author : Leonida Gianfagna
language : en
Publisher: Springer Nature
Release Date : 2021-04-28
Explainable Ai With Python written by Leonida Gianfagna and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-28 with Computers categories.
This book provides a full presentation of the current concepts and available techniques to make “machine learning” systems more explainable. The approaches presented can be applied to almost all the current “machine learning” models: linear and logistic regression, deep learning neural networks, natural language processing and image recognition, among the others. Progress in Machine Learning is increasing the use of artificial agents to perform critical tasks previously handled by humans (healthcare, legal and finance, among others). While the principles that guide the design of these agents are understood, most of the current deep-learning models are "opaque" to human understanding. Explainable AI with Python fills the current gap in literature on this emerging topic by taking both a theoretical and a practical perspective, making the reader quickly capable of working with tools and code for Explainable AI. Beginning with examples of what Explainable AI (XAI) is and why it is needed in the field, the book details different approaches to XAI depending on specific context and need. Hands-on work on interpretable models with specific examples leveraging Python are then presented, showing how intrinsic interpretable models can be interpreted and how to produce “human understandable” explanations. Model-agnostic methods for XAI are shown to produce explanations without relying on ML models internals that are “opaque.” Using examples from Computer Vision, the authors then look at explainable models for Deep Learning and prospective methods for the future. Taking a practical perspective, the authors demonstrate how to effectively use ML and XAI in science. The final chapter explains Adversarial Machine Learning and how to do XAI with adversarial examples.
Explainable Ai With Python
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Author : Antonio Di Cecco
language : en
Publisher: Springer Nature
Release Date : 2025-08-04
Explainable Ai With Python written by Antonio Di Cecco and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-04 with Computers categories.
This comprehensive book on Explainable Artificial Intelligence has been updated and expanded to reflect the latest advancements in the field of XAI, enriching the existing literature with new research, case studies, and practical techniques. The Second Edition expands on its predecessor by addressing advancements in AI, including large language models and multimodal systems that integrate text, visual, auditory, and sensor data. It emphasizes making complex systems interpretable without sacrificing performance and provides an enhanced focus on additive models for improved interpretability. Balancing technical rigor with accessibility, the book combines theory and practical application to equip readers with the skills needed to apply explainable AI (XAI) methods effectively in real-world contexts. Features: Expansion of the "Intrinsic Explainable Models" chapter to delve deeper into generalized additive models and other intrinsic techniques, enriching the chapter with new examples and use cases for a better understanding of intrinsic XAI models. Further details in "Model-Agnostic Methods for XAI" focused on how explanations differ between the training set and the test set, including a new model to illustrate these differences more clearly and effectively. New section in "Making Science with Machine Learning and XAI" presenting a visual approach to learning the basic functions in XAI, making the concept more accessible to readers through an interactive and engaging interface. Revision in "Adversarial Machine Learning and Explainability" that includes a code review to enhance understanding and effectiveness of the concepts discussed, ensuring that code examples are up-to-date and optimized for current best practices. New chapter on "Generative Models and Large Language Models (LLM)" chapter dedicated to generative models and large language models, exploring their role in XAI and how they can be used to create richer, more interactive explanations. This chapter also covers the explainability of transformer models and privacy through generative models. New "Artificial General Intelligence and XAI" mini-chapter dedicated to exploring the implications of Artificial General Intelligence (AGI) for XAI, discussing how advancements towards AGI systems influence strategies and methodologies for XAI. Enhancements in "Explaining Deep Learning Models" features new methodologies in explaining deep learning models, further enriching the chapter with cutting-edge techniques and insights for deeper understanding.
Hands On Explainable Ai Xai With Python
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Author : Denis Rothman
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-07-31
Hands On Explainable Ai Xai With Python written by Denis Rothman and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-31 with Computers categories.
Resolve the black box models in your AI applications to make them fair, trustworthy, and secure. Familiarize yourself with the basic principles and tools to deploy Explainable AI (XAI) into your apps and reporting interfaces. Key FeaturesLearn explainable AI tools and techniques to process trustworthy AI resultsUnderstand how to detect, handle, and avoid common issues with AI ethics and biasIntegrate fair AI into popular apps and reporting tools to deliver business value using Python and associated toolsBook Description Effectively translating AI insights to business stakeholders requires careful planning, design, and visualization choices. Describing the problem, the model, and the relationships among variables and their findings are often subtle, surprising, and technically complex. Hands-On Explainable AI (XAI) with Python will see you work with specific hands-on machine learning Python projects that are strategically arranged to enhance your grasp on AI results analysis. You will be building models, interpreting results with visualizations, and integrating XAI reporting tools and different applications. You will build XAI solutions in Python, TensorFlow 2, Google Cloud’s XAI platform, Google Colaboratory, and other frameworks to open up the black box of machine learning models. The book will introduce you to several open-source XAI tools for Python that can be used throughout the machine learning project life cycle. You will learn how to explore machine learning model results, review key influencing variables and variable relationships, detect and handle bias and ethics issues, and integrate predictions using Python along with supporting the visualization of machine learning models into user explainable interfaces. By the end of this AI book, you will possess an in-depth understanding of the core concepts of XAI. What you will learnPlan for XAI through the different stages of the machine learning life cycleEstimate the strengths and weaknesses of popular open-source XAI applicationsExamine how to detect and handle bias issues in machine learning dataReview ethics considerations and tools to address common problems in machine learning dataShare XAI design and visualization best practicesIntegrate explainable AI results using Python modelsUse XAI toolkits for Python in machine learning life cycles to solve business problemsWho this book is for This book is not an introduction to Python programming or machine learning concepts. You must have some foundational knowledge and/or experience with machine learning libraries such as scikit-learn to make the most out of this book. Some of the potential readers of this book include: Professionals who already use Python for as data science, machine learning, research, and analysisData analysts and data scientists who want an introduction into explainable AI tools and techniquesAI Project managers who must face the contractual and legal obligations of AI Explainability for the acceptance phase of their applications
Practical Explainable Ai Using Python
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Author : Pradeepta Mishra
language : en
Publisher:
Release Date : 2022
Practical Explainable Ai Using Python written by Pradeepta Mishra and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Learn the ins and outs of decisions, biases, and reliability of AI algorithms and how to make sense of these predictions. This book explores the so-called black-box models to boost the adaptability, interpretability, and explainability of the decisions made by AI algorithms using frameworks such as Python XAI libraries, TensorFlow 2.0+, Keras, and custom frameworks using Python wrappers. You'll begin with an introduction to model explainability and interpretability basics, ethical consideration, and biases in predictions generated by AI models. Next, you'll look at methods and systems to interpret linear, non-linear, and time-series models used in AI. The book will also cover topics ranging from interpreting to understanding how an AI algorithm makes a decision Further, you will learn the most complex ensemble models, explainability, and interpretability using frameworks such as Lime, SHAP, Skater, ELI5, etc. Moving forward, you will be introduced to model explainability for unstructured data and natural language processing-related tasks. Additionally, the book looks at counterfactual explanations for AI models. Practical Explainable AI Using Python shines the light on deep learning models, rule-based expert systems, and computer vision tasks using various XAI frameworks. You will: Review the different ways of making an AI model interpretable and explainable Examine the biasness and good ethical practices of AI models Quantify, visualize, and estimate reliability of AI models Design frameworks to unbox the black-box models Assess the fairness of AI models Understand the building blocks of trust in AI models Increase the level of AI adoption.
Explainable Ai Recipes
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Author : Pradeepta Mishra
language : en
Publisher:
Release Date : 2023
Explainable Ai Recipes written by Pradeepta Mishra and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
Understand how to use Explainable AI (XAI) libraries and build trust in AI and machine learning models. This book utilizes a problem-solution approach to explaining machine learning models and their algorithms. The book starts with model interpretation for supervised learning linear models, which includes feature importance, partial dependency analysis, and influential data point analysis for both classification and regression models. Next, it explains supervised learning using non-linear models and state-of-the-art frameworks such as SHAP values/scores and LIME for local interpretation. Explainability for time series models is covered using LIME and SHAP, as are natural language processing-related tasks such as text classification, and sentiment analysis with ELI5, and ALIBI. The book concludes with complex model classification and regression-like neural networks and deep learning models using the CAPTUM framework that shows feature attribution, neuron attribution, and activation attribution. After reading this book, you will understand AI and machine learning models and be able to put that knowledge into practice to bring more accuracy and transparency to your analyses. You will: Create code snippets and explain machine learning models using Python Leverage deep learning models using the latest code with agile implementations Build, train, and explain neural network models designed to scale Understand the different variants of neural network models.
Interpretability And Explainability In Ai Using Python Decrypt Ai Decision Making Using Interpretability And Explainability With Python To Build Reliable Machine Learning Systems
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Author : Aruna Chakkirala
language : en
Publisher: Orange Education Pvt Limited
Release Date : 2025-04-15
Interpretability And Explainability In Ai Using Python Decrypt Ai Decision Making Using Interpretability And Explainability With Python To Build Reliable Machine Learning Systems written by Aruna Chakkirala and has been published by Orange Education Pvt Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-15 with Computers categories.
Demystify AI Decisions and Master Interpretability and Explainability Today Key Features● Master Interpretability and Explainability in ML, Deep Learning, Transformers, and LLMs● Implement XAI techniques using Python for model transparency● Learn global and local interpretability with real-world examples Book DescriptionInterpretability in AI/ML refers to the ability to understand and explain how a model arrives at its predictions. It ensures that humans can follow the model's reasoning, making it easier to debug, validate, and trust. Interpretability and Explainability in AI Using Python takes you on a structured journey through interpretability and explainability techniques for both white-box and black-box models. You’ll start with foundational concepts in interpretable machine learning, exploring different model types and their transparency levels. As you progress, you’ll dive into post-hoc methods, feature effect analysis, anchors, and counterfactuals—powerful tools to decode complex models. The book also covers explainability in deep learning, including Neural Networks, Transformers, and Large Language Models (LLMs), equipping you with strategies to uncover decision-making patterns in AI systems. Through hands-on Python examples, you’ll learn how to apply these techniques in real-world scenarios. By the end, you’ll be well-versed in choosing the right interpretability methods, implementing them efficiently, and ensuring AI models align with ethical and regulatory standards—giving you a competitive edge in the evolving AI landscape. What you will learn● Dissect key factors influencing model interpretability and its different types.● Apply post-hoc and inherent techniques to enhance AI transparency.● Build explainable AI (XAI) solutions using Python frameworks for different models.● Implement explainability methods for deep learning at global and local levels.● Explore cutting-edge research on transparency in transformers and LLMs.● Learn the role of XAI in Responsible AI, including key tools and methods.
Interpretable Ai
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Author : Ajay Thampi
language : en
Publisher: Simon and Schuster
Release Date : 2022-07-05
Interpretable Ai written by Ajay Thampi and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-05 with Computers categories.
AI doesn't have to be a black box. These practical techniques help shine a light on your model's mysterious inner workings. Make your AI more transparent, and you'll improve trust in your results, combat data leakage and bias, and ensure compliance with legal requirements. Interpretable AI opens up the black box of your AI models. It teaches cutting-edge techniques and best practices that can make even complex AI systems interpretable. Each method is easy to implement with just Python and open source libraries. You'll learn to identify when you can utilize models that are inherently transparent, and how to mitigate opacity when your problem demands the power of a hard-to-interpret deep learning model.
Explainable Ai For Practitioners
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Author : Michael Munn
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2022-10-31
Explainable Ai For Practitioners written by Michael Munn and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-31 with Computers categories.
Most intermediate-level machine learning books focus on how to optimize models by increasing accuracy or decreasing prediction error. But this approach often overlooks the importance of understanding why and how your ML model makes the predictions that it does. Explainability methods provide an essential toolkit for better understanding model behavior, and this practical guide brings together best-in-class techniques for model explainability. Experienced machine learning engineers and data scientists will learn hands-on how these techniques work so that you'll be able to apply these tools more easily in your daily workflow. This essential book provides: A detailed look at some of the most useful and commonly used explainability techniques, highlighting pros and cons to help you choose the best tool for your needs Tips and best practices for implementing these techniques A guide to interacting with explainability and how to avoid common pitfalls The knowledge you need to incorporate explainability in your ML workflow to help build more robust ML systems Advice about explainable AI techniques, including how to apply techniques to models that consume tabular, image, or text data Example implementation code in Python using well-known explainability libraries for models built in Keras and TensorFlow 2.0, PyTorch, and HuggingFace
Interpretability And Explainability In Ai Using Python
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Author : Aruna Chakkirala
language : en
Publisher: Orange Education Pvt Ltd
Release Date : 2025-04-15
Interpretability And Explainability In Ai Using Python written by Aruna Chakkirala and has been published by Orange Education Pvt Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-15 with Computers categories.
TAGLINE Demystify AI Decisions and Master Interpretability and Explainability Today KEY FEATURES ● Master Interpretability and Explainability in ML, Deep Learning, Transformers, and LLMs ● Implement XAI techniques using Python for model transparency ● Learn global and local interpretability with real-world examples DESCRIPTION Interpretability in AI/ML refers to the ability to understand and explain how a model arrives at its predictions. It ensures that humans can follow the model's reasoning, making it easier to debug, validate, and trust. Interpretability and Explainability in AI Using Python takes you on a structured journey through interpretability and explainability techniques for both white-box and black-box models. You’ll start with foundational concepts in interpretable machine learning, exploring different model types and their transparency levels. As you progress, you’ll dive into post-hoc methods, feature effect analysis, anchors, and counterfactuals—powerful tools to decode complex models. The book also covers explainability in deep learning, including Neural Networks, Transformers, and Large Language Models (LLMs), equipping you with strategies to uncover decision-making patterns in AI systems. Through hands-on Python examples, you’ll learn how to apply these techniques in real-world scenarios. By the end, you’ll be well-versed in choosing the right interpretability methods, implementing them efficiently, and ensuring AI models align with ethical and regulatory standards—giving you a competitive edge in the evolving AI landscape. WHAT WILL YOU LEARN ● Dissect key factors influencing model interpretability and its different types. ● Apply post-hoc and inherent techniques to enhance AI transparency. ● Build explainable AI (XAI) solutions using Python frameworks for different models. ● Implement explainability methods for deep learning at global and local levels. ● Explore cutting-edge research on transparency in transformers and LLMs. ● Learn the role of XAI in Responsible AI, including key tools and methods. WHO IS THIS BOOK FOR? This book is tailored for Machine Learning Engineers, AI Engineers, and Data Scientists working on AI applications. It also serves as a valuable resource for professionals and students in AI-related fields looking to enhance their expertise in model interpretability and explainability techniques. TABLE OF CONTENTS 1. Interpreting Interpretable Machine Learning 2. Model Types and Interpretability Techniques 3. Interpretability Taxonomy and Techniques 4. Feature Effects Analysis with Plots 5. Post-Hoc Methods 6. Anchors and Counterfactuals 7. Interpretability in Neural Networks 8. Explainable Neural Networks 9. Explainability in Transformers and Large Language Models 10. Explainability and Responsible AI Index
Ethical Ai Responsible Machine Learning With Python
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Author : Pythquill Publishing
language : en
Publisher: Independently Published
Release Date : 2025-07
Ethical Ai Responsible Machine Learning With Python written by Pythquill Publishing and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07 with Computers categories.
What You Will Learn in This Book Understand the Principles of Ethical AI: Learn why ethics is critical in AI development and how principles like fairness, transparency, accountability, and privacy can be practically applied to machine learning systems. Identify and Address Bias in Machine Learning: Discover the different sources and types of bias in datasets and models, and gain hands-on skills to detect, measure, and interpret bias using Python-based tools and metrics. Apply Fairness Metrics to Evaluate ML Models: Learn how to choose and implement fairness metrics such as demographic parity, equal opportunity, and predictive equality to assess the ethical impact of your models. Implement Bias Mitigation Techniques: Explore proven pre-processing, in-processing, and post-processing strategies to reduce unfairness in machine learning systems, and apply them using tools like AIF360 and Fairlearn. Build Explainable AI Systems: Understand the importance of explainability in AI and how to make machine learning models more interpretable using both model-specific and model-agnostic techniques with libraries such as LIME and SHAP. Visualize and Communicate Model Behavior: Gain practical experience generating visual explanations and summaries that help stakeholders understand model decisions, improve trust, and meet compliance standards. Strengthen ML Privacy and Security: Learn how to protect user data and mitigate privacy risks by implementing techniques like differential privacy, federated learning, and homomorphic encryption in your ML workflows. Evaluate and Defend Against Adversarial Threats: Understand common adversarial attacks on machine learning models and apply countermeasures to improve model robustness using Python libraries such as ART and CleverHans. Design Accountable AI Workflows: Discover how to create audit-ready documentation artifacts like model cards and datasheets, and incorporate traceability and reproducibility into your development pipeline. Integrate Ethics into MLOps Pipelines: Learn how to operationalize responsible AI practices by embedding fairness, explainability, and privacy checks into continuous integration and deployment (CI/CD) systems. Monitor and Maintain Ethical AI in Production: Develop strategies for tracking model performance and fairness over time, detecting ethical drift, and retraining models responsibly as data evolves. Foster Responsible AI Culture in Organizations: Explore how diverse teams, ethical review boards, and clear communication practices can help build a sustainable and accountable AI development culture. Apply Python to Real-World Responsible AI Projects: Work through end-to-end case studies that apply responsible AI principles to real-world scenarios in finance, healthcare, recommender systems, and NLP. Stay Informed on AI Ethics Trends and Regulations: Gain awareness of current and emerging global AI regulations, ethical frameworks, and industry standards that impact how AI systems are built and governed. Build a Long-Term Ethical AI Skillset: Equip yourself with tools, resources, and best practices to continue learning and adapting in the rapidly evolving field of ethical AI and responsible machine learning.