<![CDATA[The increasing reliance on artificial intelligence (AI) in decision-making systems has raised critical concerns regarding transparency, interpretability, and trust. Many advanced AI models, particularly deep learning techniques, operate as opaque “black-box” systems, making it difficult for users to understand how decisions are derived. This lack of explainability limits user confidence, hinders accountability, and poses ethical and regulatory challenges. This study addresses these issues by exploring the role of Explainable Artificial Intelligence (XAI) in enhancing transparency in decision-making systems. The research is conceptually supported by three key stages illustrated in the figures. First, opaque AI systems are examined, highlighting the limitations of black-box models that provide output without meaningful explanations. Second, an XAI framework is introduced, demonstrating how interpretability techniques such as feature importance analysis, rule-based reasoning, and model-agnostic explanation methods can reveal the internal logic of AI systems. These techniques enable users to understand the reasoning behind predictions, thereby improving system interpretability. Third, the study presents the outcome of integrating XAI into decision-making processes, emphasizing transparent and accountable systems that foster trust, fairness, and user engagement. A comparative methodological approach is adopted, evaluating both traditional black-box models and explainable models using interpretability and performance metrics. The findings indicate that while there may be trade-offs between accuracy and interpretability, the inclusion of XAI significantly enhances user understanding and trust in AI-driven decisions. In conclusion, this study demonstrates that explainable AI plays a vital role in transforming opaque decision-making systems into transparent and accountable frameworks. By bridging the gap between complex algorithms and human understanding, XAI supports the development of trustworthy and ethically aligned AI systems suitable for real-world applications.]]>
Copyrights © 2023
