Artificial intelligence plays a crucial role determining the decisions businesses make. In these cases, when a machine makes a decision, humans usually want to understand whether the decision is authentic or whether it was generated in error. If business stakeholders are not convinced by the decision, they will not trust the machine learning system, and hence artificial intelligence adoption will gradually reduce within that organization. To make the decision process more transparent, developers must be able to document the explainability of AI decisions or ML model decisions. This book provides a series of solutions to problems that require explainability and interpretability. Adopting an AI model and developing a responsible AI system requires explainability as a component.

This book covers model interpretation for supervised learning linear models, including important features for regression and classification models, partial dependency analysis for regression and classification models, and influential data point analysis for both classification and regression models. Supervised learning models using nonlinear models is explored using state-of-the-art frameworks such as SHAP values/scores, including global explanation, and how to use LIME for local interpretation. This book will also give you an understanding of bagging, boosting-based ensemble models for supervised learning such as regression and classification, as well as explainability for time-series models using LIME and SHAP, natural language processing tasks such as text classification, and sentiment analysis using ELI5, ALIBI. The most complex models for classification and regression, such as neural network models and deep learning models, are explained using the CAPTUM framework, which shows feature attribution, neuron attribution, and activation attribution.

This book attempts to make AI models explainable to help developers increase the adoption of AI-based models within their organizations and bring more transparency to decision-making. After reading this book, you will be able to use Python libraries such as Alibi, SHAP, LIME, Skater, ELI5, and CAPTUM. Explainable AI Recipes provides a problem-solution approach to demonstrate each machine learning model, and shows how to use Python’s XAI libraries to answer questions of explainability and build trust with AI models and machine learning models. All source code can be downloaded from github.com/apress/explainable-ai-recipes.