Interpretability Toolkits and Fairness Measures – AWS, GCP, Azure, and AIF 360
Throughout this book, we have discussed the rationale for why responsible AI and AI governance are increasingly becoming critical disciplines for enterprises that wish to leverage AI. We also provided an overview of the methods that can be used to test that the machine learning models underpinning AI are safe, fair, and fit for purpose, along with introducing an AI assurance framework – AI STEPS FORWARD.
Leading cloud AI providers – the hyperscalers (namely AWS, Google, and Microsoft) – have recognized the need for AI explainability, and each has developed explainability toolkits designed to be used with its respective ML/AI development and MLOps environments. At the time of writing, the use of these explainability toolkits is not widespread across the industry. We believe that this is due to the following:
- The relative immaturity of the widespread industry when it...
