E. López Cano, J. Saugar, C. LANCHO MARTIN, M. Cuesta, I. Martín de Diego, A. Amado
Counterfactual explanations are a well-known technique in Explainable Machine Learning (XML) to provide simple explanations on complex Machine Learning (ML) models. Through understandable "what if" scenarios, counterfactuals explore how changes in the input data affect the results of a model. This work leverages counterfactual explanations for sustainable tourism. The proposed method analyzes the relationships between several Sustainable Tourism Indicators (STIs) defined for a specific tourist destination and its general sustainability assessment, identifying the key changes needed in the STIs to achieve an improved global sustainability score. As a result, a Decision Support System (DSS) is offered for sustainable tourism management, which domain experts can use to make more informed decisions. The DSS has been implemented in a multilingual Shiny application and includes exploration, visualization, and analysis of open data.
Keywords: Shiny, R, Counterfactuals· Explainable Machine Learning, Explainable Artificial Intelligence, Sustainable Tourism
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