[DMANET] [CFP] Special Session on Evolutionary Computation for Explainable and Trustworthy AI @ EvoApps 2027 (EvoStar)

Dear Colleagues, We are pleased to invite submissions to the Special Session on *Evolutionary Computation for Explainable and Trustworthy AI*, held as part of *EvoApps 2027* at the upcoming *EvoStar 2027* conference. Call for Papers Artificial Intelligence systems are increasingly deployed in domains where predictive accuracy alone is insufficient—decisions must also be understandable, reliable, robust, and open to scrutiny. Many explainability and trustworthiness problems involve competing objectives, complex search spaces, discrete or structured representations, and constraints that traditional optimization methods struggle to address. Evolutionary computation is particularly well-suited to these challenges. Population-based search can generate diverse explanations, explicitly balance criteria such as fidelity, stability, diversity, and plausibility, operate with non-differentiable models, and optimize symbolic, rule-based, visual, prototype-based, or counterfactual representations. This special session provides a focused forum for researchers developing evolutionary and bio-inspired approaches to the generation, optimization, evaluation, and auditing of explanations and intrinsically interpretable AI systems. Scope & Topics of Interest We welcome methodological contributions, evaluation studies, benchmarks, and real-world applications across machine learning, computer vision, image processing, and related areas, including (but not limited to): - *Explanation Generation:* Evolutionary generation and optimization of local/global, counterfactual, causal recourse, and actionable explanations. - *Interpretable Models:* Genetic programming, symbolic regression, rule learning, fuzzy systems, and neuroevolution for transparent or self-explainable models. - *Representation & Discovery:* Prototype-, example-, concept-, and case-based explanations, as well as feature selection/construction for explainability. - *Computer Vision & Multimodal AI:* Evolutionary visual explanations for image processing, computer vision, deep models, and foundation models. - *Trustworthiness & Auditing:* Evolutionary optimization and analysis of robustness, fairness, uncertainty, bias, and automated testing of black-box models. - *Specialized Approaches:* Quality-diversity methods for multiple valid explanations, human-in-the-loop/interactive evolutionary explainability, and explainable evolutionary reinforcement learning. - *Evaluation:* Benchmarks, metrics, reproducibility, scalability, and user-centered evaluation. More Information: https://www.evostar.org/2027/evoapps/ecxtai/ Organizers - *Diego Oliva* — Universidad de Guadalajara, Mexico ( diego.oliva@cucei.udg.mx) - *Oscar Ramos-Soto* — Universidad de Guadalajara, Mexico ( oscar.ramos9279@alumnos.udg.mx) - *Fernando Lezama* — GECAD – Polytechnic of Porto, Portugal ( flz@isep.ipp.pt) - *Saul Zapotecas-Martínez* — Instituto Nacional de Astrofísica, Óptica y Electrónica, Mexico (szapotecas@inaoep.mx) We look forward to receiving your contributions! Feel free to forward this call to colleagues who may be interested. Best regards, *The Session Organizing Committee* ********************************************************** * * Contributions to be spread via DMANET are submitted to * * DMANET@zpr.uni-koeln.de * * Replies to a message carried on DMANET should NOT be * addressed to DMANET but to the original sender. The * original sender, however, is invited to prepare an * update of the replies received and to communicate it * via DMANET. * * DISCRETE MATHEMATICS AND ALGORITHMS NETWORK (DMANET) * http://www.zaik.uni-koeln.de/AFS/publications/dmanet/ * **********************************************************