MAINLE: A Multi-Agent, Interactive, Natural Language Local Explainer of Classification Tasks

Serafim, P.B. et al. (2026). MAINLE: A Multi-Agent, Interactive, Natural Language Local Explainer of Classification Tasks. In: Ribeiro, R.P., et al. Machine Learning and Knowledge Discovery in Databases. Research Track. ECML PKDD 2025. Lecture Notes in Computer Science, vol 16016. Springer, Cham. DOI: 10.1007/978-3-032-06078-5_9

Abstract: There is an increasing need to explain machine learning decisions in an understandable way, even for non-expert users. In this paper, we introduce a multi-agent architecture to provide interactive explanations for classification tasks based on a range of machine learning algorithms, so that end-users can obtain answers in natural language. Our architecture is composed of four agents that are able to convert any classifier into a surrogate Decision Tree around the neighbourhood of a classification instance, which is then translated into a natural language explanation that can be further explored in an interactive way. We validate our approach against publicly available datasets using different classification methods, discussing the relevance of the architecture along five quality attributes, and performing a user study to evaluate the generated explanations. Our results show that the proposed architecture is able to generate simplified explanations that are more understandable for non-expert users in comparison to the ones given directly by a single explainer in all evaluated criteria.