Tam Chuem Vai, CarlosMykhayliv, Volodymyr2024-10-282024-10-282024-10-24http://hdl.handle.net/10362/174168Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe efficiency of Machine Learning (ML) algorithms and projects presents a tremendous opportunity for better decision-making in the activity of financial institutions. However, the growing complexity of ML algorithms proves to be a considerable obstacle to the widespread adoption of these technologies due to the lack of transparency and interpretability, deterring users from understanding the reasoning for the decisions made by the model. Despite the clear value and success of these ML models, uncertainty and concerns from end users intensify this issue, forcing them to blindly trust the outcomes, especially in the context of Natural Language Processing (NLP) projects. At Banco de Portugal, the implementation of classification models for information requests has been very successful. However, the lack of explainability of some results poses an obstacle to the full adoption of some models by the business, resulting in constant skepticism about the results and fear of misinformation. We propose a framework that systematically addresses these challenges, with the final goal of overcoming these obstacles is a crucial step to develop trust, reduce doubt and ensure a smooth integration of ML technologies in the decision-making practices of financial institutions.engExplainable artificial intelligencenatural language processingevaluation methodstrustworthy artificial intelligenceexplainabilitySDG 9 - Industry, innovation and infrastructureSDG 12 - Responsible production and consumptionSDG 17 - Partnerships for the goalsmaster thesis203778154