Enhancing Transparency through Explainable Artificial Intelligence: An Exploratory Analysis on Collusion and Corruption Scenario in Digital Government

The use of artificial intelligence (AI) systems is significantly increased in the past few years. AI system is expected to provide accurate predictions and it is also crucial that the decisions made by the AI systems are humanly interpretable i.e. anyone must be able to understand and comprehend the results produced by the AI system. AI systems are being implemented even for simple decision support and are easily accessible to the common man on the tip of their fingers. The increase in usage of AI has come with its own limitation, i.e. its interpretability. This work contributes towards the use of explainability methods such as local interpretable model-agnostic explanations (LIME) to interpret the results of various black box models. The conclusion is that, the bidirectional long short-term memory (LSTM) model is superior for sentiment analysis. The operations of a random forest classifier, a black box model, using explainable artificial intelligence (XAI) techniques like LIME is used in this work. The features used by the random forest model for classification are not entirely correct. The use of LIME made this possible. The proposed model can be used to enhance performance, which raises the trustworthiness and legitimacy of AI systems.

XAI is critical for establishing trust and enabling the appropriate development of machine learning models. By offering transparency into how these models make judgements, XAI enables researchers and users to uncover potential biases, admit limits, and eventually enhance the fairness and dependability of AI systems. In this paper, we demonstrates two techniques, LIME and SHAP, used to improve the interpretability of machine learning models. Assessing Explainable AI (XAI) approaches is critical in searching for transparent and interpretable artificial intelligence (AI) models. Explainable AI (XAI) approaches are designed to provide insight into how complex models make decisions. This paper thoroughly analyzes two prominent XAI methods: Shapley Additive explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). This study aims to understand the decision made by a machine learning model and how the model came to that decision. We discuss the approaches and framework of both LIME and SHAP and assess their behavior in predicting the model's outcome.

Introduction: The integration of Artificial Intelligence (AI) into personalized banking has enhanced service delivery in areas such as loan processing, credit assessment, and fraud detection. Despite these advancements, ethical concerns, especially algorithmic bias and lack of fairness, pose significant challenges. This study addresses the need for equitable AI systems that promote transparency, fairness, and regulatory compliance in the banking sector. Objective: This study aims to develop and implement a comprehensive framework for integrating ethical principles into AI-driven banking systems, with a focus on mitigating algorithmic bias, enhancing fairness, and improving transparency in personalized banking services. Methods: A comprehensive methodology is proposed that integrates bias-aware data collection, fairness-constrained machine-learning models, and explainable AI (XAI) techniques. Tools such as Shapley Additive Explanations (SHAPs) and Local Interpretable Model-Agnostic Explanations (LIMEs) are applied to interpret model outputs. Adversarial debiasing and fairness-aware learning algorithms were employed to identify and mitigate systemic biases in financial data. Alternative data sources, including utility and rental payment histories, were incorporated to enhance inclusivity. Results: The implementation of the proposed framework demonstrates improved fairness in decision-making without significantly compromising model accuracy. Bias metrics show measurable reductions in disparate impacts across the demographic groups. Explainability tools enhance transparency, enabling a more transparent communication of AI decisions to both users and regulators. Conclusions: Embedding ethical principles into AI-driven banking systems is critical to ensuring fairness, regulatory alignment, and public trust. The structured framework presented in this study supports the development of responsible AI systems to mitigate bias, enhance explainability, and foster financial inclusion. This approach serves as the foundation for building equitable and accountable AI applications in modern banking.

The increasing sophistication and frequency of cyberattacks necessitate advanced and transparent decision-making frameworks for threat intelligence and risk assessment. While artificial intelligence (AI) has demonstrated significant potential in automating threat detection, predicting attack patterns, and prioritizing incident response, the opaque nature of many AI models particularly deep learning poses challenges in trust, interpretability, and regulatory compliance. This paper presents an Explainable AI (XAI)-driven framework for enhancing Cyber Threat Intelligence (CTI) and risk assessment, enabling security analysts to understand, validate, and act upon AI-generated insights with confidence. The proposed approach integrates state-of-the-art machine learning algorithms with model-agnostic and model-specific interpretability techniques, such as SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), Layer-wise Relevance Propagation (LRP), and attention-based visualization. The framework processes diverse CTI data sources including network traffic logs, vulnerability feeds, malware signatures, and open-source intelligence (OSINT) to identify malicious patterns, quantify risks, and generate actionable intelligence. By coupling predictive accuracy with transparency, the system not only improves detection and classification of advanced persistent threats (APTs), phishing campaigns, and zero-day exploits, but also facilitates compliance with emerging AI governance frameworks, such as the EU AI Act and NIST AI Risk Management Framework. Experimental evaluations on benchmark cybersecurity datasets demonstrate that the XAI-enabled models maintain high precision and recall while offering interpretable outputs that significantly reduce analyst decision latency and improve collaborative threat response. The paper further discusses the role of XAI in mitigating bias, enhancing accountability, and fostering human–machine teaming in security operations centers (SOCs). Practical implementation considerations including scalability, computational efficiency, and integration with existing Security Information and Event Management (SIEM) platforms are addressed, along with recommendations for future research in multi-modal XAI for cybersecurity. This research underscores the critical value of explainability in AI-driven CTI, ensuring that automated systems are not only effective but also transparent, trustworthy, and aligned with organizational and regulatory requirements for responsible AI deployment in cyber defense.

Explainable artificial intelligence for predicting dengue outbreaks in Bangladesh using eco-climatic triggers.

BackgroundTuberculosis spondylitis (TS), commonly known as Pott’s disease, is a severe type of skeletal tuberculosis that typically requires surgical treatment. However, this treatment option has led to an increase in healthcare costs due to prolonged hospital stays (PLOS). Therefore, identifying risk factors associated with extended PLOS is necessary. In this research, we intended to develop an interpretable machine learning model that could predict extended PLOS, which can provide valuable insights for treatments and a web-based application was implemented.MethodsWe obtained patient data from the spine surgery department at our hospital. Extended postoperative length of stay (PLOS) refers to a hospitalization duration equal to or exceeding the 75th percentile following spine surgery. To identify relevant variables, we employed several approaches, such as the least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE) based on support vector machine classification (SVC), correlation analysis, and permutation importance value. Several models using implemented and some of them are ensembled using soft voting techniques. Models were constructed using grid search with nested cross-validation. The performance of each algorithm was assessed through various metrics, including the AUC value (area under the curve of receiver operating characteristics) and the Brier Score. Model interpretation involved utilizing methods such as Shapley additive explanations (SHAP), the Gini Impurity Index, permutation importance, and local interpretable model-agnostic explanations (LIME). Furthermore, to facilitate the practical application of the model, a web-based interface was developed and deployed.ResultsThe study included a cohort of 580 patients and 11 features include (CRP, transfusions, infusion volume, blood loss, X-ray bone bridge, X-ray osteophyte, CT-vertebral destruction, CT-paravertebral abscess, MRI-paravertebral abscess, MRI-epidural abscess, postoperative drainage) were selected. Most of the classifiers showed better performance, where the XGBoost model has a higher AUC value (0.86) and lower Brier Score (0.126). The XGBoost model was chosen as the optimal model. The results obtained from the calibration and decision curve analysis (DCA) plots demonstrate that XGBoost has achieved promising performance. After conducting tenfold cross-validation, the XGBoost model demonstrated a mean AUC of 0.85 ± 0.09. SHAP and LIME were used to display the variables’ contributions to the predicted value. The stacked bar plots indicated that infusion volume was the primary contributor, as determined by Gini, permutation importance (PFI), and the LIME algorithm.ConclusionsOur methods not only effectively predicted extended PLOS but also identified risk factors that can be utilized for future treatments. The XGBoost model developed in this study is easily accessible through the deployed web application and can aid in clinical research.

In the field of network security, botnet attacks pose a significant challenge, exploiting networks of infected devices to launch sophisticated threats. As these attacks evolve, the need for effective detection methods becomes increasingly critical. This study proposes an explainable Machine Learning (ML) model that aims to identify and categorize botnet attacks, and it investigates the efficiency of different Explainable Artificial Intelligence (XAI) techniques for Intrusion Detection Systems (IDS). To train our models, we employed a subset of the NCC-2 dataset, which includes a mix of normal traffic and seven different types of botnet attacks across three sensors. The ML techniques selected for this research are Random Forest (RF), Extra Trees (ET), Decision Tree (DT), and K-Nearest Neighbors (KNN), with a GridSearch cross-validation approach for optimal hyperparameter tuning. We also explored the effects of class balance through Synthetic Minority Oversampling (SMOTE) and Random Undersampling. The models’ performance was rigorously tested using the Matthews Correlation Coefficient (MCC) and Macro F1-score, with the ET model demonstrating superior results of 99% MCC and 97% F1-score, respectively. To enhance the interpretability of the ET model’s decision-making process, we integrated three XAI techniques: SHapley Additive exPlanations (SHAP), Dalex, and Local Interpretable Model-agnostic Explanations (LIME), and evaluated their efficiency. The LIME and Dalex techniques showed efficient construction times for IDS.

This study presents a comprehensive methodology for developing a Fault Detection and Diagnosis (FDD) system for an industrial distillation column using advanced machine learning algorithms. Steady state and dynamic simulations in Aspen Plus® generate extensive datasets under normal and faulty conditions. Feature engineering, using the Minimum Redundancy Maximum Relevance (MRMR) algorithm, selects the most relevant features for fault detection. Various machine learning models, including Decision Trees, Support Vector Machines, k-nearest Neighbours, and Neural Networks, were trained and evaluated based on performance metrics such as accuracy, recall, precision, and F1 score. The top models were integrated into a stacked classifier system with a voting mechanism to enhance fault detection reliability. Explainable Artificial Intelligence (XAI) techniques, such as Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP), were incorporated to improve model interpretability, allowing engineers to understand and validate the FDD system's decision-making process. Simulation results confirm that the proposed methodology accurately identifies and classifies faults. By integrating dynamic simulations, advanced machine learning, and XAI techniques, a robust and scalable solution is achieved for fault detection in distillation columns, improving operational reliability, safety, and reducing downtime. Future work could extend this approach to other industrial processes and explore additional machine learning algorithms to further enhance performance.

The rise of sophisticated cyber threats, such as malware and network intrusions, necessitates the use of Artificial Intelligence (AI) for efficient and accurate detection. However, traditional AI models often operate as black boxes, leaving security analysts without insights into the reasoning behind critical decisions. Explainable AI (XAI) addresses this challenge by providing interpretability and transparency in AI-driven cybersecurity solutions. This paper explores the role of XAI in malware analysis and network intrusion detection, highlighting how interpretable models enhance trust, improve decision-making, and facilitate regulatory compliance. It examines state-of-the-art XAI techniques, including Shapley Additive Explanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), and rule-based systems, for their application in identifying malicious software and detecting network anomalies. Furthermore, the paper discusses challenges, such as computational overhead and scalability, while presenting future directions for integrating XAI in real-time and hybrid security frameworks. By advancing the adoption of interpretable AI, cybersecurity systems can achieve greater effectiveness and reliability, addressing both technical and organizational needs in combating evolving cyber threats.

The rapid proliferation of artificial intelligence (AI) technologies across various industries and decision-making processes has undeniably transformed the way of approaching complex problems and tasks. AI systems have proven their prowess in areas such as healthcare, finance, and autonomous systems, revolutionizing how decisions are made. Nevertheless, this proliferation of AI has raised critical concerns regarding the transparency, accountability, and fairness of these systems, as many of the state-of-the-art AI models often resemble complex black boxes. These intricate models, particularly deep learning neural networks, harbor non-linear relationships that are difficult for human users to decipher, thereby raising concerns about bias, fairness, and overall trustworthiness in AI-driven decisions. The urgency of this issue is underscored by the realization that AI should not merely be accurate; it should also be interpretable. Explainable AI (XAI) has emerged as a vital field of research, emphasizing the development of models and techniques that render AI systems comprehensible and transparent in their decision-making processes. This paper investigates into the relevance and significance of XAI across various domains, including healthcare, finance, and autonomous systems, where the ability to understand the rationale behind AI decisions is paramount. In healthcare, where AI assists in diagnosis and treatment, the interpretability of AI models is crucial for clinicians to make informed decisions. In finance, applications like credit scoring and investment analysis demand transparent AI to ensure fairness and accountability. In the realm of autonomous systems, transparency is indispensable to guarantee safety and compliance with regulations. Moreover, government agencies in areas such as law enforcement and social services require interpretable AI to maintain ethical standards and accountability. This paper also highlights the diverse array of research efforts in the XAI domain, spanning from model-specific interpretability methods to more general approaches aimed at unveiling complex AI models. Interpretable models like decision trees and rule-based systems have gained attention for their inherent transparency, while integrating explanation layers into deep neural networks strives to balance accuracy with interpretability. The study emphasizes the significance of this burgeoning field in bridging the gap between AI's advanced capabilities and human users' need for comprehensible AI systems. It seeks to contribute to this field by exploring the design, development, and practical applications of interpretable AI models and techniques, with the ultimate goal of enhancing the trust and understanding of AI-driven decisions.

Electrocardiogram signal analysis can be time-consuming, tedious, and error-prone. Therefore, automated analysis is need of time that will assist clinicians in detecting cardiac abnormalities accurately and efficiently. Recently, deep learning models have shown unprecedented progress and strong arrhythmia classification capabilities, but their deployment in the medical sector is constrained due to their “black-box” nature. This paper proposes a robust explainability method to assist in explaining the underlying decision-making process in deep neural networks (DNN) and help provide feedback on biases that would benefit in improving DNN models. To achieve these objectives, initially, a deep learning model is trained on the MIT-BIH Arrhythmia Database and their classification performance is evaluated. The classification findings are then interpreted using the post-hoc explanation methods such as the SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME) and Gradient-weighted Class Activation Mapping (Grad-CAM) that interpret the decision rationale. As these methods are initially proposed for image applications therefore a new masking approach is proposed to cater these post-hoc explainability methods for ECG time-series data. After evaluating these methods for ECG arrhythmia classification, several drawbacks are drawn such as they fail to locate a feature’s importance if there are multiple occurrences of the same feature in a signal and also, SHAP and LIME perform random perturbations that sometimes produce unreliable explanations. Therefore, to overcome the drawbacks associated with these post-hoc explainability methods on time-series data, a novel K-GradCam method is proposed. The proposed K-GradCam method ensemble the benefits of these gradient based and perturbation based approaches and has demonstrated advantages over SHAP, Grad-CAM and LIME in terms of interpreting the models’ decisions. To compare the the proposed technique with post-hoc explainability methods quantitatively, the confidence index of the proposed method is evaluated using dice loss. The proposed method shares 71% similarity with SHAP and 81% similarity with Grad-CAM methods; however, it shares the benefits of both methods and is computationally faster than SHAP and LIME.

Blood pressure abnormality detection and Interpretation utilizing Explainable Artificial Intelligence

Species distribution models (SDMs) remotely guide conservation programs for endangered species by estimating potential reserve areas based on a set of environmental features. Most SDM research only explains their predictions across the study area (global), effectively disregarding the predictions for specific sites (local) where conservation-related activities are confined. This study aims to address this spatial gap in explainability by applying model-agnostic post-hoc methods in explainable artificial intelligence for SDM at two scopes. These methods explain the importance, effects, and interactions of bioclimatic features on the SDM for Mindoro warty pigs (Sus oliveri), an emblematic yet endangered endemic fauna in Mindoro Island, Philippines. Areas with a high predicted probability of presence coincide with higher elevation, spanning the Mindoro Mountain Range. Global explainability methods-Permutation Feature Importance, Shapley Additive Explanations (SHAP), and Accumulated Local Effect-reveal that annual precipitation mostly accounts for this island-wide trend, with more rain corresponding to higher probabilities. This is also observed using local explainability methods-SHAP, Local Interpretable Model-agnostic Explanations, and Break Down-for the respective predictions on three potential conservation sites. The cumulative effect of bioclimatic features in these ~ 1km2 sites-within Mts. Iglit-Baco National Park, Upper Amnay Watershed, and Mt. Calavite Wildlife Sanctuary-is a decrease in the predicted probability of presence. This calls for improved local monitoring of Mindoro warty pig populations. While building upon our ongoing efforts for its conservation in Mindoro Island, this study also extends the pipeline for SDM using explainability methods, thereby opening a new axis for interpreting SDM predictions.

Competitor analysis is a fundamental requirement in both strategic and operational management, and the competitive attributes of reviewer comments are a crucial determinant of competitor analysis approaches. Most studies have focused on identifying competitors or detecting comparative sentences, not competitive attributes. Thus, the authors propose a method based on explainable artificial intelligence (XAI) that can detect competitive attributes from consumers’ perspectives. They construct a model to classify the reviewer comments for each competitive product and calculate the importance of each keyword in the reviewer comments during the classification process. This is based on the assumption that keywords significantly influence product classification. The authors also propose an additional novel methodology that combines various XAI techniques such as local interpretable model-agnostic explanations, Shapley additive explanations, logistic regression, gradient-based class activation map, and layer-wise relevance propagation to build a robust model for calculating the importance of competitive attributes for various data sources.

This research proposes a novel strategy for accurate breast lesion classification that combines explainable artificial intelligence (XAI), machine learning (ML) classifiers, and customized weakly dependent features from ultrasound (BU) images. Two new weakly dependent feature classes are proposed to improve the diagnostic accuracy and diversify the training data. These are based on image intensity variations and the area of bounded partitions and provide complementary rather than overlapping information. ML classifiers such as Random Forest (RF), Extreme Gradient Boosting (XGB), Gradient Boosting Classifiers (GBC), and LASSO regression were trained with both customized feature classes. To validate the reliability of our study and the results obtained, we conducted a statistical analysis using the McNemar test. Later, an XAI model was combined with ML to tackle the influence of certain features, the constraints of feature selection, and the interpretability capabilities across various ML models. LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (SHapley Additive exPlanations) models were used in the XAI process to enhance the transparency and interpretation in clinical decision-making. The results revealed common relevant features for the malignant class, consistently identified by all of the classifiers, and for the benign class. However, we observed variations in the feature importance rankings across the different classifiers. Furthermore, our study demonstrates that the correlation between dependent features does not impact explainability.