This is increasingly rely on AI to help make important decisions. But for AI to be trusted, it must be able to explain how it reached a recommendation. IBM Research is committed to building explainable AI and bringing it to the developer community. The open source AI explainability 360 toolkit offers IBM algorithms, along with demos, tutorials, guides, and other resources. Here's how a bank could use the toolkit. Different audiences require different kinds of explanation. When a customer's loan application is rejected, a bank's data scientist can use an algorithm from the toolkit to check whether the AI is properly weighing risk factors. Meanwhile, another algorithm can help a loan officer analyze whether customers with similar scores repaid or defaulted on their loans. A customer service tool built using a different explainability algorithm could explain risk factors and prioritization and suggest how to improve the customer's credit score. With the AI explainability 360 toolkit, IBM Research helps the open source community build more transparent and trustworthy AI. We invite you to use it and improve it.