Symbolic Knowledge Extraction from Opaque Machine Learning Predictors: GridREx & PEDRO


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Federico Sabbatini, Roberta Calegari

Gabriele Kern-Isberner, Gerhard Lakemeyer, Thomas Meyer (eds.)
“19th International Conference on Principles of Knowledge Representation and Reasoning”, pages 554–563
IJCAI Organization
August 2022

Procedures aimed at explaining outcomes and behaviour of opaque predictors are becoming more and more essential as machine learning (ML) black-box (BB) models pervade a wide variety of fields and, in particular, critical ones – e.g., medical or financial –, where it is not possible to make decisions on the basis of a blind automatic prediction. A growing number of methods designed to overcome this BB limitation
is present in the literature, however some ML tasks are nearly or completely neglected—e.g., regression and clustering. Furthermore, existing techniques may be not applicable in complex real-world scenarios or they can affect the output predictions with undesired artefacts.
In this paper we present the design and the implementation of GridREx, a pedagogical algorithm to extract knowledge from black-box regressors, along with PEDRO, an optimisation procedure to automate the GridREx hyper-parameter tuning phase with better results than manual tuning. We also report the results of our experiments involving the application of GridREx and PEDRO in real case scenarios, including GridREx performance assessment by using as benchmarks other similar state-of-the-art techniques. GridREx proved to be able to give more concise explanations with higher fidelity and predictive capabilities.

(keywords) Explainable AI, Integrating knowledge representation and machine learning, Integrating symbolic and sub-symbolic approaches, Applications of KR

Talks

Events

  • 19th International Conference on Principles of Knowledge Representation and Reasoning (KR 2022) — 31/07/2022–05/08/2022

Publication

— authors

— editors

Gabriele Kern-Isberner, Gerhard Lakemeyer, Thomas Meyer

— status

published

— sort

paper in proceedings

— publication date

August 2022

— volume

19th International Conference on Principles of Knowledge Representation and Reasoning

— pages

554–563

identifiers

— DOI

10.24963/kr.2022/57

— IRIS

11585/893144

— print ISSN

2334-1033

— print ISBN

978-1-956792-01-0

files

Open Access PDF

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