Report

Abstract

AI-based automated decisions are increasingly used as part of new services being deployed to the general public. This approach to building services presents significant potential benefits, such as the reduced speed of execution, increased accuracy, lower cost, and ability to learn from a wide variety of situations. Of course, equally significant concerns have been raised and are now well documented such as concerns about privacy, fairness, bias and ethics.

In this project, we have implemented a Loan Decision scenario, instrumented its decision pipeline so that it records provenance, categorised explanations according to their audience and their purpose, built an explanation-generation prototype, and wrapped the whole system in an online demonstrator. This work aimed to demonstrate that provenance, defined as a record that describes the people, institutions, entities, and activities involved in producing, influencing, or delivering a decision, is a solid foundation for generating its explanations.