Getting started with hyperscaler interpretability toolkits

Hyperscalers have built significant offerings to provide Explainable AI to address bias and model quality and assess risk exposure. These tools are typically built around a fairness engine, and explainability visual interfaces are used for a variety of different enterprises in an industry-agnostic manner. Their MLOps platforms also help automate AI monitoring to ensure responsible AI outcomes and synthesized data as a means of fairness, privacy, confidentiality, and bias mitigation.

There is a growing need for AI explainability tools that can help users understand how AI algorithms make decisions, especially for subject-matter experts. Explainable AI tools provide insights into the inner workings of an AI system, allowing users to see how the algorithms arrive at their results. We have provided a checklist for selecting an explainability platform here.

Who would use these Explainable AI toolkits?

Explainable AI toolkits...