@inproceedings{cb9e62e61e73442fb3a537f7a16971a0,
title = "Towards Incorporating Human Knowledge in Fuzzy Pattern Tree Evolution",
abstract = "This paper shows empirically that Fuzzy Pattern Trees (FPT) evolved using Grammatical Evolution (GE), a system we call FGE, meet the criteria to be considered a robust Explainable Artificial Intelligence (XAI) system. Experimental results show FGE achieves competitive results against state of the art black box methods on a set of real world benchmark problems. Various selection methods were investigated to see which was best for finding smaller, more interpretable models and a human expert was recruited to test the interpretability of the models found and to give a confidence score for each model. Models which were deemed interpretable but not trustworthy by the expert were seen to be outperformed in classification accuracy by interpretable models which were judge trustworthy, validating that FGE can be a powerful XAI technique.",
keywords = "Explainable AI, Fuzzy logic, Grammatical Evolution",
author = "Aidan Murphy and Gr{\'a}inne Murphy and Jorge Amaral and Douglas MotaDias and Enrique Naredo and Conor Ryan",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 24th European Conference on Genetic Programming, EuroGP 2021 ; Conference date: 07-04-2021 Through 09-04-2021",
year = "2021",
doi = "10.1007/978-3-030-72812-0_5",
language = "English",
isbn = "9783030728113",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "66--81",
editor = "Ting Hu and Nuno Louren{\c c}o and Eric Medvet",
booktitle = "Genetic Programming - 24th European Conference, EuroGP 2021, Held as Part of EvoStar 2021, Proceedings",
}