On generating trustworthy counterfactual explanations Del Ser, Javier Barredo Arrieta, Alejandro Díaz Rodríguez, Natalia Ana Herrera Triguero, Francisco Saranti, Anna Holzinger, Andreas Explainable artificial intelligence Deep learning Counterfactual explanations Deep learning models like chatGPT exemplify AI success but necessitate a deeper understanding of trust in critical sectors. Trust can be achieved using counterfactual explanations, which is how humans become familiar with unknown processes; by understanding the hypothetical input circumstances under which the output changes. We argue that the generation of counterfactual explanations requires several aspects of the generated counterfactual instances, not just their counterfactual ability. We present a framework for generating counterfactual explanations that formulate its goal as a multiobjective optimization problem balancing three objectives: plausibility; the intensity of changes; and adversarial power. We use a generative adversarial network to model the distribution of the input, along with a multiobjective counterfactual discovery solver balancing these objectives. We demonstrate the usefulness of six classification tasks with image and 3D data confirming with evidence the existence of a trade-off between the objectives, the consistency of the produced counterfactual explanations with human knowledge, and the capability of the framework to unveil the existence of concept-based biases and misrepresented attributes in the input domain of the audited model. Our pioneering effort shall inspire further work on the generation of plausible counterfactual explanations in real-world scenarios where attribute-/concept-based annotations are available for the domain under analysis. 2024-05-06T10:16:53Z 2024-05-06T10:16:53Z 2023-11-17 journal article J. Del Ser, A. Barredo-Arrieta, N. Díaz-Rodríguez et al. Information Sciences 655 (2024) 119898 [https://doi.org/10.1016/j.ins.2023.119898] https://hdl.handle.net/10481/91428 10.1016/j.ins.2023.119898 eng info:eu-repo/grantAgreement/EC/H2020/826078 http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional Elsevier