Exploring the Interpretability of Sequential Predictions Through Rationale Model

Explainable Artificial Intelligence (XAI) has been a crucial research area recently, particularly for applications where transparency and interpretability are critical. In this paper, we propose a method for generating interpretable rationales for sequential predictions made by machine learning model. Our approach involves an enhanced sequential prediction model and a rationale generator that produces human-readable explanations for the model's predictions. We evaluate our method on two datasets, the Reddit Comments dataset and the Predictive Toxicology Challenge dataset, and demonstrate the robustness of the system in generating accurate predictions with useful rationales. The rationales generated by our method are relevant to the prediction and can be easily understood by humans. Our proposed method has potential applications in various domains, including healthcare and legal decision-making, where interpretability and transparency are important. We believe our approach can advance the field of XAI and contribute to the development of more trustworthy machine learning models.