Interperetable AI
- Length: 275 pages
- Edition: 1
- Language: English
- Publisher: Manning
- Publication Date: 2022-02-22
- ISBN-10: 161729764X
- ISBN-13: 9781617297649
- Sales Rank: #785550 (See Top 100 Books)
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.
Interpretable AI brief content contents preface acknowledgments about this book Who should read this book How this book is organized: a roadmap About the code liveBook discussion forum about the author about the cover illustration Part 1: Interpretability basics Chapter 1: Introduction 1.1 Diagnostics+ AI—an example AI system 1.2 Types of machine learning systems 1.2.1 Representation of data 1.2.2 Supervised learning 1.2.3 Unsupervised learning 1.2.4 Reinforcement learning 1.2.5 Machine learning system for Diagnostics+ AI 1.3 Building Diagnostics+ AI 1.4 Gaps in Diagnostics+ AI 1.4.1 Data leakage 1.4.2 Bias 1.4.3 Regulatory noncompliance 1.4.4 Concept drift 1.5 Building a robust Diagnostics+ AI system 1.6 Interpretability vs. explainability 1.6.1 Types of interpretability techniques 1.7 What will I learn in this book? 1.7.1 What tools will I be using in this book? 1.7.2 What do I need to know before reading this book? Chapter 2: White-box models 2.1 White-box models 2.2 Diagnostics+—diabetes progression 2.3 Linear regression 2.3.1 Interpreting linear regression 2.3.2 Limitations of linear regression 2.4 Decision trees 2.4.1 Interpreting decision trees 2.4.2 Limitations of decision trees 2.5 Generalized additive models (GAMs) 2.5.1 Regression splines 2.5.2 GAM for Diagnostics+ diabetes 2.5.3 Interpreting GAMs 2.5.4 Limitations of GAMs 2.6 Looking ahead to black-box models Part 2: Interpreting model processing Chapter 3: Model-agnostic methods: Global interpretability 3.1 High school student performance predictor 3.1.1 Exploratory data analysis 3.2 Tree ensembles 3.2.1 Training a random forest 3.3 Interpreting a random forest 3.4 Model-agnostic methods: Global interpretability 3.4.1 Partial dependence plots 3.4.2 Feature interactions Chapter 4: Model-agnostic methods: Local interpretability 4.1 Diagnostics+ AI: Breast cancer diagnosis 4.2 Exploratory data analysis 4.3 Deep neural networks 4.3.1 Data preparation 4.3.2 Training and evaluating DNNs 4.4 Interpreting DNNs 4.5 LIME 4.6 SHAP 4.7 Anchors Chapter 5: Saliency mapping 5.1 Diagnostics+ AI: Invasive ductal carcinoma detection 5.2 Exploratory data analysis 5.3 Convolutional neural networks 5.3.1 Data preparation 5.3.2 Training and evaluating CNNs 5.4 Interpreting CNNs 5.4.1 Probability landscape 5.4.2 LIME 5.4.3 Visual attribution methods 5.5 Vanilla backpropagation 5.6 Guided backpropagation 5.7 Other gradient-based methods 5.8 Grad-CAM and guided Grad-CAM 5.9 Which attribution method should I use? Part 3: Interpreting model representations Chapter 6: Understanding layers and units 6.1 Visual understanding 6.2 Convolutional neural networks: A recap 6.3 Network dissection framework 6.3.1 Concept definition 6.3.2 Network probing 6.3.3 Quantifying alignment 6.4 Interpreting layers and units 6.4.1 Running network dissection 6.4.2 Concept detectors 6.4.3 Concept detectors by training task 6.4.4 Visualizing concept detectors 6.4.5 Limitations of network dissection Chapter 7: Understanding semantic similarity 7.1 Sentiment analysis 7.2 Exploratory data analysis 7.3 Neural word embeddings 7.3.1 One-hot encoding 7.3.2 Word2Vec 7.3.3 GloVe embeddings 7.3.4 Model for sentiment analysis 7.4 Interpreting semantic similarity 7.4.1 Measuring similarity 7.4.2 Principal component analysis (PCA) 7.4.3 t-distributed stochastic neighbor embedding (t-SNE) 7.4.4 Validating semantic similarity visualizations Part 4: Fairness and bias Chapter 8: Fairness and mitigating bias 8.1 Adult income prediction 8.1.1 Exploratory data analysis 8.1.2 Prediction model 8.2 Fairness notions 8.2.1 Demographic parity 8.2.2 Equality of opportunity and odds 8.2.3 Other notions of fairness 8.3 Interpretability and fairness 8.3.1 Discrimination via input features 8.3.2 Discrimination via representation 8.4 Mitigating bias 8.4.1 Fairness through unawareness 8.4.2 Correcting label bias through reweighting 8.5 Datasheets for datasets Chapter 9: Path to explainable AI 9.1 Explainable AI 9.2 Counterfactual explanations appendix A: Getting set up A.1 Python A.2 Git code repository A.3 Conda environment A.4 Jupyter notebooks A.5 Docker appendix B: PyTorch B.1 What is PyTorch? B.2 Installing PyTorch B.3 Tensors B.3.1 Data types B.3.2 CPU and GPU tensors B.3.3 Operations B.4 Dataset and DataLoader B.5 Modeling B.5.1 Automatic differentiation B.5.2 Model definition B.5.3 Training index A B C D E F G H I J K L M N O P Q R S T U V W X
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