Pradeepta Mishra - Practical Explainable AI Using Python: Artificial Intelligence Model Explanations Using Python-based Libraries, Extensions, and Frameworks

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Explainable artificial intelligent (XAI) is a current need as more and more AI models are in production to generate business decisions. Thus, many users are also getting impacted by these decisions. One user may get favorably or unfavorably impacted. As a result, its important to know the key features leading to these decisions. It is often argued that AI models are quite black-box in nature because the AI models decisions cannot be explained, hence the adoptability of AI models is quite slow in the industry. This book is an attempt at unboxing the so-called black-box models to increase 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. The objective of this book is to explain the AI models in simple language using the above mentioned frameworks.

Model interpretability and explainability are the key focuses of this book. There are mathematical formulas and methods that are typically used to explain a decision made by an AI model. You will be provided with software library methods, classes, frameworks, and functions and how to use them for model explainability, transparency, reliability, ethics, bias, and interpretability. If a human being can understand the reasons behind the decision made by the AI model, it will give much more power to the user to make amendments and recommendations. There are two different kinds of users: business users and practitioners. The business user wants the explainability in simple language without any statistical or mathematical terms. The practitioner wants to know the explainability from the computational point of view. This book is an attempt at balancing both needs when generating explanations.

This book begins with an introduction to model explainability and interpretability basics, ethical considerations in AI applications, and biases in predictions generated by AI models. Then you will learn about the reliability of AI models in generating predictions in different use cases. Then you will explore the methods and systems to interpret the linear models, non-linear models, and time series models used in AI. Next, you will learn about the most complex ensemble models, explainability, and interpretability using frameworks such as Lime, SHAP, Skater, ELI5, and Alibi. Then you will learn about model explainability for unstructured data and natural language processing related to text classification tasks. Examining model fairness also requires a simulation of what-if scenarios using the prediction outcomes. Youll cover this next. Then you will read about counterfactual and contrastive explanations for AI models. You will explore model explainability for deep learning models, rule-based expert systems, and model-agnostic explanations for prediction invariance and for computer vision tasks using various XAI frameworks.

Today we have AI engineers and data scientists who train or build these models; software developers who put these models into production and thus operationalize the models; business users who consume the end result or outcome generated by the models; and decision makers who think about the decisions made by the models. The leadership in driving AI projects/products think, Is there any way to have clarity around the models and predictive modelers? Bio-statisticians of course think how explain the model predictions, etc. The expectation is to develop an explainability framework that caters to the needs of all stakeholders involved in this process of making AI work in real life. Again, this book strikes a balance between multiple stakeholders. It leans towards data scientists, because if a data scientist is at least convinced about the explainability, they can explain further to the business stakeholder.

Making AI models explainable to the business user in simple, plain language will take some time. Perhaps some new framework will come along to address this. At this moment, the challenge is that the data scientist who built the model doesnt have complete clarity about the models behavior and lacks clarity in explaining the AI model. Newly trained data scientists or graduating data scientists will get a tremendous benefit from this book. Similarly, other AI engineers will also benefit from this book. This is an evolving area; this books explanations were current in July, 2021.

This book is based on the research conducted on the available frameworks around model explainability for making black box-based artificial intelligence models into white box models and making the decisions made by AI models transparent. I am grateful to my friends and family for encouraging me to start the work, persevere with the work, and get to the final step of publishing it.

I thank my wife, Prajna, for her continuous encouragement and support in pushing me to complete the book, helping me to prioritize the book over vacations, taking care of the kids, and allowing me enough time to Complete the book.

I thank my editor, Divya, who has been a continuous support throughout writing the book, being flexible with timelines and giving me enough time to complete this book.