## 1 Introduction
While automated detection tools are crucial in identifying complex social issues such as sexism, recent models such as transformers function as black boxes, offering little to no insight into the reasoning behind their classifications. As highlighted in the review by [PERSON] et al. [15], and the analysis of online content challenges by [PERSON] [16], while the lack of transparency in these models does not necessarily affect their accuracy in detecting sexist content, the 'black box' nature of these models makes it difficult to understand their rationale and identify potential errors. This lack of transparency and explainability can lead to mistrust from users, which in turn might reduce their willingness to engage with or rely on the system, thereby undermining its overall effectiveness in detecting online sexism. The need to understand the decision process of the models has led to the development of various techniques to make these models more interpretable, as highlighted in recent surveys on the state of explainable natural language processing (XNLP) tasks [17; 18]. XNLP offers an exciting response to the challenges of interpretability and trust in the context of online harm detection, including sexism detection. XNLP enables users to understand and critically evaluate the outcomes of automated detection systems by providing insights into the decision-making process of AI models. This is particularly important in sensitive fields like the detection of sexism, where the importance of false positives or negatives exceeds simple errors. In this scenario, a false negative (failure to identify a sexist comment) poses the risk of overlooking harmful behavior, whereas a false positive (incorrectly labeling a statement as sexist) may wrongly implicate individuals or content. These errors carry significant ethical and social consequences, in addition to being crucial from a data accuracy perspective. Our research proposes the application of the XNLP technique to detect and understand sexism in social media posts. By prioritizing explainability, we aim to clarify the decision-making processes of algorithms, particularly when dealing with large language models(LLMs). Some researchers have tackled the problem of detecting sexist text using ensemble models, as shown by [PERSON] et al. [19] in their study on ensembling transformer models for identifying online sexism. Their approach combines multiple transformer models to improve detection accuracy. We also propose an ensemble method for this task; however, our method incorporates explainability techniques such as SHAP values to provide insights into the decision-making process of each model, offering a more transparent and interpretable solution compared to the previous work. The ensemble approach has advantages because it combines predictions from multiple pre-trained models, such as Bidirectional Encoder Representations from Transformers (BERT) and DistilBERT, enhancing the overall performance of the system. In summary, our study aims to contribute to the growing body of research on sexism detection by providing a novel ensemble approach that integrates technical accuracy with explainability. The remainder of this paper is organized as follows: we start with a review of the most relevant works in Section 2. Then, we describe the data and our methodology in Section 3, which includes the data analysis, data preparation, and augmentation, and our ensemble model design; we then introduce our approach about explainability analysis in Section 3.5. Subsequently, we discuss experimentation and results in Section 4, covering the hyperparameter tuning of the model and training progress, along with several examples of how explainability impacts the performance metrics. The paper concludes with the discussion, conclusion, and future work in Section 5.