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

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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.

hosting event
reference publication
page_white_acrobatOn the Design of PSyKE: A Platform for Symbolic Knowledge Extraction (paper in proceedings, 2021) — Federico Sabbatini, Giovanni Ciatto, Roberta Calegari, Andrea Omicini
funding project
wrenchEXPECTATION — Personalized Explainable Artificial Intelligence for decentralized agents with heterogeneous knowledge (01/04/2021–31/03/2024)
wrenchStairwAI — Stairway to AI: Ease the Engagement of Low-Tech users to the AI-on-Demand platform through AI (01/01/2021–31/12/2023)
works as
reference talk for
page_white_acrobatPreface (editorial/introduction/preface, 2021) — Roberta Calegari, Giovanni Ciatto, Enrico Denti, Andrea Omicini, Giovanni Sartor
page_white_acrobatOn the Design of PSyKE: A Platform for Symbolic Knowledge Extraction (paper in proceedings, 2021) — Federico Sabbatini, Giovanni Ciatto, Roberta Calegari, Andrea Omicini