Titlebook: Responsible AI; Implementing Ethical Sray Agarwal,Shashin Mishra Book 2021 The Editor(s) (if applicable) and The Author(s), under exclusive

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Explainability,This chapters covers AI explainability from three lenses: explaining features, explaining models and creating explainable models. For each of these approaches, it goes deep into the detail of how you can utilize them, the scenarios that make one option preferred over the others and multiple ways of applying them.

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Accountability in AI,In this chapter, we will look at the concept of model drift and the types of drifts – data and concept. For each of these, we will then explain the various methods that can be used by the business analysts and the data scientists alike to determine if a drift is happening and ways to determine the right method to apply given a specific problem.

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Sray Agarwal,Shashin MishraHands-on approach to ensure easy practical implementation of the concepts discussed.Most of the techniques covered are new, with only a few that refer to existing packages. For the techniques covered,

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978-3-030-76859-1The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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Remove Bias from ML Model, be used with most of the machine learning algorithms. These techniques offer a lot of scope for optimization and the business analysts in the team will have to take the lead in understanding and deciding the best approach for the optimization that can be achieved.

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Data and Model Privacy,sensitive parameters in the data but also help reduce the bias. This chapter explains why you should be considering data and model privacy as an integral part of your responsible AI journey and how you can apply it for the problem you are working on.

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