A Case-Based Explanation System for ‘Black-Box’ Systems

Abstract

Most users of machine-learning products are reluctant to use the systems without any sense of the underlying logic that has led to the system’s predictions. Unfortunately many of these systems lack any transparency in the way they operate and are deemed to be ‘black boxes’. In this paper we present a Case-Based Reasoning (CBR) solution to providing supporting explanations of black-box systems. This CBR solution uses locally derived feature ranking information that reflects the importance of each feature to a prediction and a locally adjusted case retrieval mechanism. The retrieval mechanism takes advantage of the derived feature weightings to help select cases that are a better reflection of the black-box solution and thus more convincing explanations. “Computers are useless. They can only give you answers.” - Pablo Picasso.