## 5 Discussion and Conclusions  
The present study introduces a new methodology for detecting sexism in textual content by using explainability to define a Sexism Score. This approach integrates ensemble modelling and SHAP values for understanding and identifying sexist language. Our methodology shows advancements in the field of sexism detection. The ensemble model design, CustomBERT, which combined various BERT versions with a CNN architecture,  
\begin{table}
\begin{tabular}{l l l l l l} \hline
**Dataset Portion** & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **Runtime (s)** \\ \hline All Sentences & 0.79 & 0.77 & 0.79 & 0.76 & 1578 (26.3 min) \\ High Sexism Score (Top 80\%) & 0.79 & 0.75 & 0.76 & 0.73 & 1342 (22.4 min) \\ \hline \end{tabular}
\end{table}
Table 9: Efficiency comparison.  
capitalized on the strengths of multiple transformer models, enhancing the overall accuracy and robustness of the system.  
The explainability analysis using SHAP values provided a deeper understanding of the model's decision-making process. By identifying the most influential tokens, we could assign a meaningful Sexism Score to each text entry, thereby offering a transparent and interpretable metric for sexism detection. This aspect is crucial, as it not only improves the trustworthiness of the model but also aids in highlighting specific elements within the text that contribute to its classification as sexist.  
Experimental results indicated that our CustomBERT model outperforms individual transformer models across all tasks. Also, the implementation of Sexism Scores based on SHAP values showed a clear correlation between these scores and model performance. Texts with higher Sexism Scores were more reliably identified as sexist, highlighting the efficacy of our explainability-driven approach. Moreover, the efficiency improvements observed by prioritizing high-scoring sentences underscore the practical benefits of this methodology in real-world applications, where processing time and computational resources are critical considerations.  
In conclusion, this study presents an interpretable framework for sexism detection in textual content. By integrating a sophisticated ensemble modeling approach, and a thorough explainability analysis, we have developed a model that provides valuable insights into its decision-making process. The introduction of Sexism Scores enhances the model's transparency and interpretability, making it a valuable tool for both academic research and practical applications in combating online harassment and promoting respectful discourse.  
Future work can explore the application of this methodology to other forms of hate speech and biased language, further refining the explainability components to address diverse linguistic and cultural contexts. Additionally, integrating user feedback into the explainability analysis could enhance the model's adaptability and accuracy, ensuring its continued relevance and effectiveness in dynamic online environments.  
Conceptualization, H.M., A.G. and A.B.; Methodology, H.M., A.G. and A.B.; Resources, H.M.; Writing-original draft, H.M.; Writing-review & editing, A.G. and A.B.; Supervision, A.G. and A.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This data can be found here: ([[https://github.com/rewire-online/edos](https://github.com/rewire-online/edos)]([https://github.com/rewire-online/edos](https://github.com/rewire-online/edos))).  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AI & Artificial Intelligence \\ BERT & Bidirectional Encoder Representations from Transformers \\ CNN & Convolutional Neural Network \\ LLM & Large Language Model \\ NLP & Natural Language Processing \\ SHAP & Shapley Additive Explanations \\ XAI & Explainable Artificial Intelligence \\ XNLP & Explainable Natural Language Processing \\ \end{tabular}