Symbolic knowledge extraction from opaque ML predictors in PSyKE: Platform design & experiments


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Federico Sabbatini, Giovanni Ciatto, Roberta Calegari, Andrea Omicini

Intelligenza Artificiale 16(1), pp. 27–48
luglio 2022

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 proofs of concept 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 discuss 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 symbolic knowledge in logic form, allowing the extraction of first-order logic clauses. The extracted knowledge is thus both machine- and human-interpretable, and can be used as a starting point for further symbolic processing—e.g. automated reasoning.

(keywords) Explainable AI, knowledge extraction, interpretable prediction, PSyKE

Riviste & collane

Eventi

  • 22nd Workshop “From Objects to Agents” (WOA 2021) — 01/09/2021–03/09/2021

Pubblicazione

— autori/autrici

— a cura di

— stato

pubblicato

— tipo

articolo su rivista

— data di pubblicazione

luglio 2022

— rivista

Intelligenza Artificiale

— volume

16

— numero

1

— pagine

27–48

— numero di pagine

22

URL

pagina originale

identificatori

— DOI

10.3233/IA-210120

— DBLP

journals/ia/SabbatiniCCO22

— IRIS

11585/890822

— Scholar

7559675640918015038

— Scopus

2-s2.0-85134193338

— WoS / ISI

000825367300003

— print ISSN

1724-8035

— online ISSN

2211-0097

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