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 regarding the explanation of 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.

The intersection of evolutionary computation and explainable AI / Bacardit, Jaume; Brownlee, Alexander E. I.; Cagnoni, Stefano; Iacca, Giovanni; Mccall, John; Walker, David. - (2022), pp. 1757-1762. (Intervento presentato al convegno GECCO '22 tenutosi a Boston nel 9th-13th July 2022) [10.1145/3520304.3533974].

The intersection of evolutionary computation and explainable AI

Iacca, Giovanni;
2022-01-01

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 regarding the explanation of 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.
2022
GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion
New York
ACM
9781450392686
Bacardit, Jaume; Brownlee, Alexander E. I.; Cagnoni, Stefano; Iacca, Giovanni; Mccall, John; Walker, David
The intersection of evolutionary computation and explainable AI / Bacardit, Jaume; Brownlee, Alexander E. I.; Cagnoni, Stefano; Iacca, Giovanni; Mccall, John; Walker, David. - (2022), pp. 1757-1762. (Intervento presentato al convegno GECCO '22 tenutosi a Boston nel 9th-13th July 2022) [10.1145/3520304.3533974].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/351943
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