This is the repository for our paper titled "Reliable Time Series Counterfactual Explanations Guided by ShapeDBA". This paper has been accepted at 2024 IEEE International Conference on Big Data (Big Data)
Artificial intelligence (AI) and algorithmic decision-making are profoundly shaping various aspects of society, with applications in healthcare, business, education, etc. As these systems become more integral to high-stakes decisions, concerns about their transparency and interpretability are growing. To address these concerns, explainable AI (XAI) methods have been developed, with counterfactual explanations emerging as a powerful tool. Counterfactuals help users understand AI decisions by demonstrating how small changes in input could alter the outcome, providing a clear and intuitive way to interpret AI behavior. Despite their potential, generating valid, interpretable, and efficient counterfactual explanations is particularly challenging in time series domains, where data points are interdependent. In this paper, we introduce a novel approach to counterfactual explanations guided by ShapeDTW Barycenter Averaging (ShapeDBA). By integrating ShapeDBA into the counterfactual generation process, we ensure that the produced explanations are not only valid and interpretable but also efficient to generate. Our approach provides counterfactuals that align closely with human intuition while maintaining the computational efficiency required for practical deployment. This work represents a significant step forward in the development of interpretable AI systems, particularly in the complex domain of time series analysis.