On the Design of PSyKE: A Platform for Symbolic Knowledge Extraction
by Federico Sabbatini, Giovanni Ciatto, Roberta Calegari, and Andrea Omicini
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.
@inproceedings{psyke-woa2021,
articleno = 3,
author = {Sabbatini, Federico and Ciatto, Giovanni and Calegari, Roberta and Omicini, Andrea},
booktitle = {WOA 2021 -- 22nd Workshop ``From Objects to Agents''},
editor = {Calegari, Roberta and Ciatto, Giovanni and Denti, Enrico and Omicini, Andrea and Sartor, Giovanni},
issn = {1613-0073},
keywords = {explainable AI, knowledge extraction, interpretable prediction, PSyKE},
location = {Bologna, Italy},
month = oct,
note = {22nd Workshop ``From Objects to Agents'' (WOA 2021), Bologna, Italy, 1--3~} # sep # {~2021. Proceedings},
numpages = 20,
pages = {29--48},
publisher = {Sun SITE Central Europe, RWTH Aachen University},
series = {CEUR Workshop Proceedings},
subseries = {AI*IA Series},
title = {On the Design of {PSyKE}: A Platform for Symbolic Knowledge Extraction},
url = {http://ceur-ws.org/Vol-2963/paper14.pdf},
volume = 2963,
year = 2021
}