Explainability in AI

In many applications, trust in an AI system will come from its ability to ‘explain itself.’ But when it comes to understanding and explaining the inner workings of an algorithm, one size does not fit all. Different stakeholders require explanations for different purposes and objectives, and explanations must be tailored to their needs. While a regulator will aim to understand the system as a whole and probe into its logic, consumers affected by a specific decision will be interested only in factors impacting their case – for example, in a loan processing application, they will expect an explanation for why the request was denied and want to understand what changes could lead to approval.

AI Explainability 360 (AIX360) is an open source toolkit that includes algorithms that span the different dimensions of ways of explaining along with proxy explainability metrics.

In this workshop you will explore different kinds of explanations suited to different users in the context of a credit approval process enabled by machine learning. The three types of users that we will look at are a data scientist, who evaluates the machine learning model before deployment, a loan officer, who makes the final decision based on the model's output, and a bank customer, who wants to understand the reasons for their application result.

You will learn:
- how to build several machine learning models, including a simple neural network
- how to evaluate these models and their output