Transparency and granularity in the SP Theory of Intelligence and its realisation in the SP Computer Model
This addresses the need for more transparent and interpretable AI systems, though it appears incremental as it builds on an existing theoretical framework without claiming broad new breakthroughs.
The paper tackles the problem of achieving transparency and granularity in AI by describing how the SP Theory of Intelligence and its SP Computer Model can provide detailed audit trails and human-understandable knowledge structures, such as chunking-with-codes and hierarchies, to enhance interpretability and explainability.
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.