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Evolving explainable rule sets

Authors:

Hormoz Shahrzad,

Risto MiikkulainenAuthors Info & Claims

GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 1779 - 1784

https://doi.org/10.1145/3520304.3534023

Published: 19 July 2022 Publication History

Abstract

Most AI systems work like black boxes tasked with generating reasonable outputs for given inputs. Many domains, however, have explainablity and trustworthiness requirements not fulfilled by these approaches. Various methods exist to analyze or interpret black-box models post training. When it comes down to sensitive domains in which there is a mandate for white-box models, a better choice would be to use transparent models. In this work, we present a method which evolves explainable rule-sets using inherently transparent ordinary logic to make models. We showcase some sample domains we tackled and discuss their major desirable properties like bias detection, knowledge discovery, and modifiablity, to name a few.

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Cited By

Lipinski PBalcer KLi XHandl J(2024)Explaining Session-based Recommendations using Grammatical EvolutionProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3664156(1590-1597)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3664156
Bacardit JBrownlee ACagnoni SIacca GMcCall JWalker D(2023)Evolutionary Computation and Explainable AI Towards ECXAI 2023: A Year in ReviewACM SIGEVOlution10.1145/3610388.361038916:2(1-6)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3610388.3610389
Thomson SAdair JBrownlee Avan den Berg DSilva SPaquete L(2023)From Fitness Landscapes to Explainable AI and BackProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3596395(1663-1667)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583133.3596395
Show More Cited By

Index Terms

Evolving explainable rule sets
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Bio-inspired approaches
        Genetic algorithms
        Genetic programming

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cover image ACM Conferences

GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2022

2395 pages

ISBN:9781450392686

DOI:10.1145/3520304

Editor:
Jonathan E. Fieldsend
University of Exeter
,
General Chair:
Markus Wagner
The University of Adelaide

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Published: 19 July 2022

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GECCO '22

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SIGEVO

GECCO '22: Genetic and Evolutionary Computation Conference

July 9 - 13, 2022

Massachusetts, Boston

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Lipinski PBalcer KLi XHandl J(2024)Explaining Session-based Recommendations using Grammatical EvolutionProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3664156(1590-1597)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3664156
Bacardit JBrownlee ACagnoni SIacca GMcCall JWalker D(2023)Evolutionary Computation and Explainable AI Towards ECXAI 2023: A Year in ReviewACM SIGEVOlution10.1145/3610388.361038916:2(1-6)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3610388.3610389
Thomson SAdair JBrownlee Avan den Berg DSilva SPaquete L(2023)From Fitness Landscapes to Explainable AI and BackProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3596395(1663-1667)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583133.3596395
Mei YChen QLensen AXue BZhang M(2023)Explainable Artificial Intelligence by Genetic Programming: A SurveyIEEE Transactions on Evolutionary Computation10.1109/TEVC.2022.322550927:3(621-641)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1109/TEVC.2022.3225509
Miikkulainen R(2023)Evolutionary Supervised Machine LearningHandbook of Evolutionary Machine Learning10.1007/978-981-99-3814-8_2(29-57)Online publication date: 2-Nov-2023
https://doi.org/10.1007/978-981-99-3814-8_2
Syu YWang C(2023)A Gap Between Automated Service Composition Research and Software Engineering Development Practice: Service DescriptionsWeb Services – ICWS 202310.1007/978-3-031-44836-2_2(18-31)Online publication date: 23-Sep-2023
https://dl.acm.org/doi/10.1007/978-3-031-44836-2_2

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