On the Design of PSyKE: A Platform for Symbolic Knowledge Extraction

A common practice in modern explainable AI is to post-hoc explain black-box machine learning (ML) predictors - such as neural networks - by extracting symbolic knowledge out of them, in the form of either rule lists or decision trees. By acting as a surrogate model, the extracted knowledge aims at revealing the inner working of the black box, thus enabling its inspection, representation, and explanation.

Various knowledge-extraction algorithms have been presented in the literature so far. Unfortunately, running implementations of most of them are currently either proof of concepts or unavailable. In any case, a unified, coherent software framework supporting them all - as well as their interchange, comparison, and exploitation in arbitrary ML workflows - is currently missing.

Accordingly, in this paper we present the design of PSyKE, a platform providing general-purpose support to symbolic knowledge extraction from different sorts of black-box predictors via many extraction algorithms. Notably, PSyKE targets the extraction of symbolic knowledge in logic form, making it possible to extract first-order logic clauses as output. The extracted knowledge is thus both machine- and human-interpretable, and it can be used as a starting point for further symbolic processing, e.g. automated reasoning.