Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/34194
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Author(s): Bacardit, Jaume
Brownlee, Alexander
Cagnoni, Stefano
Iacca, Giovanni
McCall, John
Walker, David
Contact Email: alexander.brownlee@stir.ac.uk
Title: The intersection of Evolutionary Computation and Explainable AI Anonymous authors
Citation: Bacardit J, Brownlee A, Cagnoni S, Iacca G, McCall J & Walker D (2022) The intersection of Evolutionary Computation and Explainable AI Anonymous authors. In: Genetic and Evolutionary Computation Conference: GECCO '22, Boston, MA, USA, 09.07.2022-13.07.2022. New York: ACM. https://gecco-2022.sigevo.org/HomePage
Date Deposited: 26-Apr-2022
Conference Name: Genetic and Evolutionary Computation Conference: GECCO '22
Conference Dates: 2022-07-09 - 2022-07-13
Conference Location: Boston, MA, USA
Abstract: In the past decade, Explainable Artificial Intelligence (XAI) has attracted a great interest in the research community, motivated by the need for explanations in critical AI applications. Some recent advances in XAI are based on Evolutionary Computation (EC) techniques , such as Genetic Programming. We call this trend EC for XAI. We argue that the full potential of EC methods has not been fully exploited yet in XAI, and call the community for future efforts in this field. Likewise, we find that there is a growing concern in EC as to what regards explaining population-based methods, i.e., their search process and outcomes. While some attempts have been done in this direction (although, in most cases, those are not explicitly put in the context of XAI), we believe that there are still several research opportunities and open research questions that, in principle, may promote a safer and broader adoption of EC in real-world applications. We call this trend XAI within EC. In this position paper, we briefly overview the main results in the two above trends, and suggest that the EC community may play a major role in the achievement of XAI. CCS CONCEPTS • Computing methodologies → Machine learning; • Theory of computation → Optimization with randomized search heuristics; • Human-centered computing → Human computer interaction (HCI).
Status: AM - Accepted Manuscript
Rights: This item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.
URL: https://gecco-2022.sigevo.org/HomePage

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