Transparency and granularity in the SP Theory of Intelligence and its realisation in the SP Computer Model

TL;DR

The paper discusses how the SP System and its computer model enhance transparency and granularity in AI through audit trails, understandable knowledge structures, and familiar organizational principles, improving interpretability and explainability.

Contribution

It introduces methods for achieving transparency in the SP System via audit trails, granular knowledge, and familiar organizational principles, advancing AI interpretability.

Findings

Audit trail provides detailed process transparency

Knowledge is structured for easy understanding

Familiar principles support interpretability

Abstract

This chapter describes how the SP System, meaning the SP Theory of Intelligence, and its realisation as the SP Computer Model, may promote transparency and granularity in AI, and some other areas of application. The chapter describes how transparency in the workings and output of the SP Computer Model may be achieved via three routes: 1) the program provides a very full audit trail for such processes as recognition, reasoning, analysis of language, and so on. There is also an explicit audit trail for the unsupervised learning of new knowledge; 2) knowledge from the system is likely to be granular and easy for people to understand; and 3) there are seven principles for the organisation of knowledge which are central in the workings of the SP System and also very familiar to people (eg chunking-with-codes, part-whole hierarchies, and class-inclusion hierarchies), and that kind of familiarity in the way knowledge is structured by the system, is likely to be important in the interpretability, explainability, and transparency of that knowledge. Examples from the SP Computer Model are shown throughout the chapter.