The potential limitations and biases in the dataset and model used in the XG-HSI framework can significantly impact the fairness and robustness of the hate speech detection system. One major limitation is the reliance on a single dataset, such as HateXplain, which may not encompass the full spectrum of hate speech directed at Islam or other marginalized communities. This can lead to a lack of generalizability, as the model may perform well on the training data but struggle with unseen data that contains different linguistic styles or contexts.
Additionally, biases in the dataset can arise from the labeling process. If the annotators have inherent biases or if the dataset is not representative of the diverse expressions of hate speech, the model may learn to misclassify certain expressions or fail to recognize subtle forms of hate speech. This can result in false positives or negatives, disproportionately affecting specific groups.
Moreover, the GNN model itself may introduce biases based on the features selected for node representation and the relationships defined in the graph. If certain words or phrases are overrepresented or underrepresented in the training data, the model may develop skewed interpretations of what constitutes hate speech.
To mitigate these issues, it is crucial to employ diverse datasets, ensure rigorous annotation processes, and continuously evaluate the model's performance across various demographic groups. Implementing fairness-aware algorithms and conducting bias audits can also help in identifying and addressing potential biases in the hate speech detection system.