Evolution of Transparent Explainable Rule-sets

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Misc{shahrzad:arxiv22,

• author = "Hormoz Shahrzad and Babak Hodjat and Risto Miikkulainen",
• title = "Evolution of Transparent Explainable Rule-sets",
• howpublished = "arXiv",
• year = "2022",
• month = "21 " # apr,
• keywords = "genetic algorithms, genetic programming, Artificial Intelligence (cs.AI), Machine Learning (cs.LG), Neural and Evolutionary Computing (cs.NE), FOS: Computer and information sciences, FOS: Computer and information sciences",
• URL = "http://nn.cs.utexas.edu/?shahrzad:arxiv22",
• URL = "https://arxiv.org/abs/2204.10438",
• DOI = "doi:10.48550/ARXIV.2204.10438",
• abstract = "Most AI systems are black boxes generating reasonable outputs for given inputs. Some domains, however, have explainability and trustworthiness requirements that cannot be directly met by these approaches. Various methods have therefore been developed to interpret black-box models after training. This paper advocates an alternative approach where the models are transparent and explainable to begin with. This approach, EVOTER, evolves rule-sets based on simple logical expressions. The approach is evaluated in several prediction/classification and prescription/policy search domains with and without a surrogate. It is shown to discover meaningful rule sets that perform similarly to black-box models. The rules can provide insight to the domain, and make biases hidden in the data explicit. It may also be possible to edit them directly to remove biases and add constraints. EVOTER thus forms a promising foundation for building trustworthy AI systems for real-world applications in the future.",

}

Genetic Programming entries for Hormoz Shahrzad Babak Hodjat Risto Miikkulainen

Citations