Titlebook: Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning; Uday Kamath,John Liu Book 2021 The Editor(s) (if a

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way. The evaluation of explanations is an interdisciplinary research covering broad areas of human-computer interaction, machine learning, psychology, cognitive science, and visualization, to name a few. This chapter first highlights some of the recent works in research to categorize and analyze the

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978-3-030-83358-9The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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Participatory Ergonomics for Return to WorkP applications and the role of interpretability. Finally, we cover computer vision and how explainability has been a focus of considerable research. We will present a case study in each domain where the reader can get practical and real-world insights.

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Explainability in Time Series Forecasting, Natural Language Processing, and Computer Vision,P applications and the role of interpretability. Finally, we cover computer vision and how explainability has been a focus of considerable research. We will present a case study in each domain where the reader can get practical and real-world insights.

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Post-Hoc Interpretability and Explanations,odel algorithms. They allow for different representations to be used for internal modeling and explanation. They can also provide different types of explanations for the same model. However, there is a trade-off between the fidelity and comprehensibility of explanations.

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XAI: Challenges and Future, cognitive science, and visualization, to name a few. This chapter first highlights some of the recent works in research to categorize and analyze the metrics in a common framework. Finally, we give some predictions on the future based on current trajectories, commercial and open-source trends, and innovations in the field.

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