Platform and Model Design for Responsible AI

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Risks and Attacks on ML Models

This chapter gives a detailed overview of defining and evaluating a Machine Learning (ML) risk framework from the instant an organization plans to embark on AI digital transformation. Risks may come in different stages, such as when the strategic or financial planning kicks in or during several of the execution phases. Risks start surfacing with the onset of technical implementations and continue up to testing phases when the AI use case is served to customers. Risk quantification can be attained through different metrics, which can certify the system behavior (amount of robustness and resiliency) against risks. In the process of understanding risk evaluation techniques, you will also get a thorough understanding ...