Explainability Assistant: A Conversational XAI Interface for Interpreting Energy Consumption Models
Energy consumption forecasting relies on increasingly complex machine learning (ML) models, such as Genetic Programming-based symbolic regressors, whose predictions can be difficult for facility managers and building operators to interpret. Explainable Artificial Intelligence (XAI) techniques address this opacity, but traditional XAI dashboards require substantial technical expertise and provide limited flexibility for dynamic, context-aware inquiry. Conversational XAI systems offer a promising alternative; however, previous approaches, such as TalkToModel, were constrained by rigid custom grammars and achieved only 76.8% intent-parsing accuracy. This paper introduces the Explainability A...
| Authors | Rodion Krjutškov, Eduard Barbu, Nikos Sakkas et al. |
|---|---|
| Primary category | cs.AI |
| Published | 10 September 2026 |
| Last updated at source | 10 September 2026 |
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