Chapter 1: Introduction to Model Explainability and InterpretabilityChapter Goal: This chapter is to understand what is model explainability and interpretability using Python. No of pages: 30-40 pages
Chapter 2: AI Ethics, Biasness and Reliability Chapter Goal: This chapter aims at covering different frameworks using XAI Python libraries to control biasness, execute the principles of reliability and maintain ethics while generating predictions.No of pages: 30-40
Chapter 3: Model Explainability for Linear Models Using XAI ComponentsChapter Goal: This chapter explains use of LIME, SKATER, SHAP and other libraries to explain the decisions made by linear models for supervised learning task, for structured dataNo of pages: 30-40
Chapter 4: Model Explainability for Non-Linear Models using XAI ComponentsChapter Goal: This chapter explains use of LIME, SKATER, SHAP and other libraries to explain the decisions made by non-linear models, such as tree based models for supervised learning task, for structured dataNo of pages: 30-40
Chapter 5: Model Explainability for Ensemble Models Using XAI Components
Chapter Goal: This chapter explains use of LIME, SKATER, SHAP and other libraries to explain the decisions made by ensemble models, such as tree based ensemble models for supervised learning task, for structured data No of pages: 30-40
Chapter 6: Model Explainability for Time Series Models using XAI ComponentsChapter Goal: This chapter explains use of LIME, SKATER, SHAP and other libraries to explain the decisions made by time series models for structured data, both univariate time series model and multivariate time series modelNo of pages: 30-40
Chapter 7: Model Explainability for Natural Language Processing using XAI ComponentsChapter Goal: This chapter explains use of LIME, SKATER, SHAP and other libraries to explain the decisions made by models from text classification, summarization, sentiment classification No of pages: 30-40
Chapter 8: AI Model Fairness Using What-If ScenarioChapter Goal: This chapter explains use of Google's WIT Tool and custom libraries to explain the fairness of an AI modelNo of pages: 30-40
Chapter 9: Model Explainability for Deep Neural Network ModelsChapter Goal: This chapter explains use of Python libraries to interpret the neural network models and deep learning models such as LSTM models, CNN models etc. using smooth grad and deep shiftNo of pages: 30-40
Chapter 10: Counterfactual Explanations for XAI modelsChapter Goal: This chapter aims at providing counterfactual explanations to explain predictions of individual instances. The "event" is the predicted outcome of an instance, the "cause" are the particular feature values of this instance that were the input to the model that "caused" a certain prediction.No of pages: 30-40
Chapter 11: Contrastive Explanation for Machine Learning
Chapter Goal: In this chapter we will use foil trees: a model-agnostic approach to extracting explanations for finding the set of rules that causes the explanation to be predicted the actual outcome (fact) instead of the other (foil)No of pages: 20-30
Chapter 12: Model-Agnostic Explanations By Identifying Prediction InvarianceChapter Goal: In this chapter we will use anchor-LIME (a-LIME), a model-agnostic technique that produces high-precision rule-based explanations for which the coverage boundaries are very clear.No of pages: 20-30
Chapter 13: Model Explainability for Rule based Exper