## 6 Discussion and Conclusions  
We have proposed the employment of two XAI models, namely, _SHAP_ and _LIME_, to enhance digital deliberation. We consider two downstream NLP applications (i.e., clustering and summarization) that can heavily augment decision making in digital environments where the amount of feedback may be enormous. While these applications have proven very helpful in deliberation analysis and can save valuable labor hours, users seem to be suspicious about the quality of their outcomes. XAI models can augment users' trust in ML models by extracting meaningful insights about the inner logic of these models and presenting them in a simplified and easily understandable form. To this end, we presented a case study, based on a dataset from the \"_Have your say_\" platform, to illustrate the outcomes of _LIME_ and _SHAP_ when utilized to explain document clustering and a summarization model.  
Aiming to advance the performance of our approach, future work will focus on improving its clustering and summarization phases by employing more sophisticated techniques to evaluate the proposed framework. Another future work direction will be the exploitation of global-based XAI techniques that will be combined with local-based approaches as a means to extract more meaningful insights from each discussion.  
The scalability of _SHAP_ models also presents a significant challenge, due to their computational intensity, particularly in large-scale and real-time applications. This issue arises from the need to evaluate the contribution of each feature by considering all possible subsets, leading to exponential growth in computations as the number of features increases. To address this, future research could focus on developing approximate SHAP methods that use sampling techniques to estimate Shapley values, thereby significantly reducing computational overhead while maintaining accuracy. Additionally, leveraging dimensionality reduction techniques and parallel computing can help decrease computation time by reducing the number of features and distributing the workload across multiple processors.  
Moreover, integrating _SHAP_ with other less computationally intensive explainability methods or newer libraries (e.g., Captum) could offer a hybrid solution that balances accuracy and efficiency. Captum, for example, provides a range of model interpretability algorithms designed to be scalable and easy to use. By exploring these avenues, it is possible to develop more efficient _SHAP_ models capable of scaling appropriately, making them suitable for online and real-time applications. This research direction promises to enhance the practical applicability of _SHAP_ in dynamic environments where quick, reliable explanations are crucial.  
Conceptualization, I.S. and N.K.; methodology, I.S.; software, I.S.; validation, I.S.; formal analysis, I.S.; investigation, I.S. and N.K.; resources, N.K.; data curation, I.S. and N.K.; writing--original draft preparation, I.S. and N.K.; writing--review and editing, I.S. and N.K.; visualization, I.S.; supervision, N.K.; project administration, N.K.; funding acquisition, N.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original data presented in the study are openly available in Github at [[https://github.com/[PERSON]/XAI-Summarization-Clustering](https://github.com/[PERSON]/XAI-Summarization-Clustering)]([https://github.com/[PERSON]/XAI-Summarization-Clustering](https://github.com/[PERSON]/XAI-Summarization-Clustering)) (accessed on 5 March 2024).  
The authors declare no conflicts of interest.