Interpretable AI : Building Explainable Machine Learning Systems

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. In Interpretable AI, you will learn: • Why AI models are hard to interpret • Interpreting white box models such as linear regression, decision trees, and generalized additive models • Partial dependence plots, LIME, SHAP and Anchors, and other techniques such as saliency mapping, network dissection, and representational learning • What fairness is and how to mitigate bias in AI systems • Implement robust AI systems that are GDPR-compliant 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. About the technology It’s often difficult to explain how deep learning models work, even for the data scientists who create them. Improving transparency and interpretability in machine learning models minimizes errors, reduces unintended bias, and increases trust in the outcomes. This unique book contains techniques for looking inside “black box” models, designing accountable algorithms, and understanding the factors that cause skewed results. About the book Interpretable AI teaches you to identify the patterns your model has learned and why it produces its results. As you read, you’ll pick up algorithm-specific approaches, like interpreting regression and generalized additive models, along with tips to improve performance during training. You’ll also explore methods for interpreting complex deep learning models where some processes are not easily observable. AI transparency is a fast-moving field, and this book simplifies cutting-edge research into practical methods you can implement with Python. What's inside • Techniques for interpreting AI models • Counteract errors from bias, data leakage, and concept drift • Measuring fairness and mitigating bias • Building GDPR-compliant AI systems About the reader For data scientists and engineers familiar with Python and machine learning. About the author Ajay Thampi is a machine learning engineer focused on responsible AI and fairness. AI doesnt have to be a black box. These practical techniques help shine a light on your models mysterious inner workings. Make your AI more transparent, and youll improve trust in your results, combat data leakage and bias, and ensure compliance with legal requirements. In Interpretable AI , you will Why AI models are hard to interpret Interpreting white box models such as linear regression, decision trees, and generalized additive models Partial dependence plots, LIME, SHAP and Anchors, and other techniques such as saliency mapping, network dissection, and representational learning What fairness is and how to mitigate bias in AI systems Implement robust AI systems that are GDPR-compliant 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. Youll 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. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Its often difficult to explain how deep learning models work, even for the data scientists who create them. Improving transparency and interpretability in machine learning models minimizes errors, reduces unintended bias, and increases trust in the outcomes. This unique book contains techniques for looking inside black box models, designing accountable algorithms, and understanding the factors that cause skewed results. About the book Interpretable AI teaches you to identify the patterns your model has learned and why it produces its results. As you read, youll pick up algorithm-specific approaches, like interpreting regression and generalized additive models, along with tips to improve performance during training. Youll also explore methods for interpreting complex deep learning models where some processes are not easily observable. AI transparency is a fast-moving field, and this book simplifies cutting-edge research into practical methods you can implement with Python. What's inside Techniques for interpreting AI models Counteract errors from bias, data leakage, and concept drift Measuring fairness and mitigating bias Building GDPR-compliant AI systems About the reader For data scientists and engineers familiar with Python and machine learning. About the author Ajay Thampi is a machine learning engineer focused on responsible AI and fairness. Table of Contents PART 1 INTERPRETABILITY BASICS 1 Introduction 2 White-box models PART 2 INTERPRETING MODEL PROCESSING 3 Model-agnostic Global interpretability 4 Model-agnostic Local interpretability 5 Saliency mapping PART 3 INTERPRETING MODEL REPRESENTATIONS 6 Understanding layers and units 7 Understanding semantic similarity PART 4 FAIRNESS AND BIAS 8 Fairness and mitigating bias 9 Path to explainable AI 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.In Interpretable AI, you will learn: Why AI models are hard to interpret Interpreting white box models such as linear regression, decision trees, and generalized additive models Partial dependence plots, LIME, SHAP and Anchors, and other techniques such as saliency mapping, network dissection, and representational learning What fairness is and how to mitigate bias in AI systems Implement robust AI systems that are GDPR-compliant 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. About the technology It's often difficult to explain how deep learning models work, even for the data scientists who create them. Improving transparency and interpretability in machine learning models minimizes errors, reduces unintended bias, and increases trust in the outcomes. This unique book contains techniques for looking inside “black box” models, designing accountable algorithms, and understanding the factors that cause skewed results. About the book Interpretable AI teaches you to identify the patterns your model has learned and why it produces its results. As you read, you'll pick up algorithm-specific approaches, like interpreting regression and generalized additive models, along with tips to improve performance during training. You'll also explore methods for interpreting complex deep learning models where some processes are not easily observable. AI transparency is a fast-moving field, and this book simplifies cutting-edge research into practical methods you can implement with Python. What's inside Techniques for interpreting AI models Counteract errors from bias, data leakage, and concept drift Measuring fairness and mitigating bias Building GDPR-compliant AI systems About the reader For data scientists and engineers familiar with Python and machine learning. About the author Ajay Thampi is a machine learning engineer focused on responsible AI and fairness. Table of Contents PART 1 INTERPRETABILITY BASICS 1 Introduction 2 White-box models PART 2 INTERPRETING MODEL PROCESSING 3 Model-agnostic methods: Global interpretability 4 Model-agnostic methods: Local interpretability 5 Saliency mapping PART 3 INTERPRETING MODEL REPRESENTATIONS 6 Understanding layers and units 7 Understanding semantic similarity PART 4 FAIRNESS AND BIAS 8 Fairness and mitigating bias 9 Path to explainable AI It's often difficult to explain how deep learning models work, even for the data scientists who create them. Improving transparency and interpretability in machine learning models minimizes errors, reduces unintended bias, and increases trust in the outcomes. This unique book contains techniques for looking inside "black box" models, designing accountable algorithms, and understanding the factors that cause skewed results. "Interpretable AI" teaches you to identify the patterns your model has learned and why it produces its results. As you read, you'll pick up algorithm-specific approaches, like interpreting regression and generalized additive models, along with tips to improve performance during training. You'll also explore methods for interpreting complex deep learning models where some processes are not easily observable. AI transparency is a fast-moving field, and this book simplifies cutting-edge research into practical methods you can implement with Python.-- Source other than the Library of Congress Interpretable AI is a hands-on guide to interpretability techniques that open up the black box of AI. AI models can become so complex that even experts have difficulty understanding them—and forget about explaining the nuances of a cluster of novel algorithms to a business stakeholder! Interpretable AI is filled with cutting-edge techniques that will improve your understanding of how your AI models function. Interpretable AI is a hands-on guide to interpretability techniques that open up the black box of AI. This practical guide simplifies cutting-edge research into transparent and explainable AI, delivering practical methods you can easily implement with Python and open source libraries. With examples from all major machine learning approaches, this book demonstrates why some approaches to AI are so opaque, teaches you to identify the patterns your model has learned, and presents best practices for building fair and unbiased models.