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Autonomous vehicles (AVs) are revolutionizing transportation by relying on advanced AI techniques like deep learning and reinforcement learning for decision-making and navigation. However, concerns about the opacity of traditional AI models in safety-critical applications such as autonomous driving raise issues related to safety, accountability, and trust. This study explores the integration of Explainable AI (XAI) techniques in AV systems to enhance transparency and interpretability while maintaining high prediction accuracy. XAI methods, such as LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive ExPlanations), provide understandable justifications for AI-driven decisions, addressing biases, fairness, and accountability. These techniques also support regulatory compliance and foster public trust in AVs. A mixed-methods approach, combining experimental simulations and user surveys, was employed to integrate XAI into AV systems and test its performance in urban traffic and highway driving scenarios. Feedback from users, collected through questionnaires and in-depth interviews, revealed that XAI-enhanced systems significantly improved the interpretability of AV decisions, leading to higher user trust and satisfaction. The study highlights the importance of balancing model complexity with interpretability, demonstrating that XAI techniques are crucial for building trust and ensuring accountability in autonomous driving systems.

Artificial Intelligence (AI) has been applied to many human endeavors, and epidemiology is no exception. The AI community has recently seen a renewed interest in applying AI methods and approaches to epidemiological problems. However, a number of challenges are impeding the growth of the field. This work reviews the uses and applications of AI in epidemiology from 1994 to 2023. The following themes were uncovered: epidemic outbreak tracking and surveillance, Geo-location and visualization of epidemics data, Tele-Health, vaccine resistance and hesitancy sentiment analysis, diagnosis, predicting and monitoring recovery and mortality, and decision support systems. Disease detection received the most interest during the time under review. Furthermore, the following AI approaches were found to be used in epidemiology: prediction, geographic information systems (GIS), knowledge representation, analytics, sentiment analysis, contagion analysis, warning systems, and classification. Finally, the work makes the following findings: the absence of benchmark datasets for epidemiological purposes, the need to develop ethical guidelines to regulate the development of AI for epidemiology as this is a major issue impeding it’s growth, a concerted and continuous collaboration between AI and Epidemiology experts to grow the field, the need to develop explainable and privacy retaining AI methods for more secured and human understandable AI solutions.

The increasing complexity and heterogeneity of data in Smart Information Systems pose significant challenges for effective decision-making. While data fusion techniques have been widely adopted to integrate multiple data sources, traditional fusion approaches often fail to consider contextual information, resulting in limited interpretability and reduced decision relevance. This study proposes a contextual data fusion approach that integrates heterogeneous data sources with contextual attributes, including temporal, spatial, and operational context, to enhance decision accuracy and robustness. The research employs a computational and experimental methodology involving data preprocessing, context encoding, multi-level data fusion, and performance evaluation. Experimental results demonstrate that the proposed approach outperforms single-source analysis and non-contextual data fusion in terms of accuracy, precision, recall, and F1-score, with only a marginal increase in computational cost. The findings confirm that incorporating context into the data fusion process significantly improves the quality and reliability of analytical outcomes. This study contributes to the development of intelligent and data-driven systems by highlighting the critical role of contextual awareness in supporting transparent and effective decision-making in Smart Information Systems.