Artificial Intelligence (AI) in Smart Agricultural Facilities (SAF) often lacks explainability, hindering farmers from taking full advantage of their capabilities. This study tackles this gap by introducing a model that combines eXplainable Artificial Intelligence (XAI), with Predictive Maintenance (PdM). The model aims to provide both predictive insights and explanations across four key dimensions, namely data, model, outcome, and end-user. This approach marks a shift in agricultural AI, reshaping how these technologies are understood and applied. The model outperforms related studies, showing quantifiable improvements. Specifically, the Long-Short-Term Memory (LSTM) classifier shows a 5.81% rise in accuracy. The eXtreme Gradient Boosting (XGBoost) classifier exhibits a 7.09% higher F1 score, 10.66% increased accuracy, and a 4.29% increase in Receiver Operating Characteristic-Area Under the Curve (ROC-AUC). These results could lead to more precise maintenance predictions in real-world settings. This study also provides insights into data purity, global and local explanations, and counterfactual scenarios for PdM in SAF. It advances AI by emphasising the importance of explainability beyond traditional accuracy metrics. The results confirm the superiority of the proposed model, marking a significant contribution to PdM in SAF. Moreover, this study promotes the understanding of AI in agriculture, emphasising explainability dimensions. Future research directions are advocated, including multi-modal data integration and implementing Human-in-the-Loop (HITL) systems aimed at improving the effectiveness of AI and addressing ethical concerns such as Fairness, Accountability, and Transparency (FAT) in agricultural AI applications.

Credit assessment remains crucial in determining an individual's creditworthiness, significantly influencing their financial opportunities. However, traditional credit assessment models have raised concerns about fairness and potential biases, which requires a rigorous evaluation and implementation of fairness in these models. This paper explores the assessment and integration of fairness in credit assessment. It explores various bias mitigation techniques applied across three critical stages: 1) pre-processing; 2) in-processing; and 3) post-processing. By systematically evaluating these techniques, the study demonstrates their effectiveness in reducing disparities and enhancing fairness to varying degrees. The findings underscore the importance of addressing age-based bias in credit assessment predictions through multiple techniques. However, a nuanced analysis is imperative to understand the trade-offs between predictive accuracy and fairness. This critical analysis not only contributes to advancing the theoretical foundations of credit assessment, but also provides practical insights for policymakers and financial institutions striving to adopt more equitable credit evaluation frameworks. Therefore, this paper contributes to the ongoing discourse on enhancing the fairness of credit assessment models, offering actionable recommendations to improve both accuracy and fairness in decision-making processes. By exploring and evaluating a spectrum of bias mitigation strategies, this paper fosters a more inclusive financial landscape where credit decisions are both transparent and accountable.