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Research papers on Machine learning interpretability

Recent and highly-cited academic work on machine learning interpretability, gathered from Semantic Scholar, CrossRef and OpenAlex.

1. Explainable AI: A Review of Machine Learning Interpretability Methods

   Pantelis Linardatos, Vasilis Papastefanopoulos, Sotiris B. Kotsiantis · 2020 · Entropy · 2,899 citations

   Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption, with machine learning systems demonstrating superhuman performance in a significant number of tasks. However, this surge in performance, has often been achieved through increased model complexity, turning such systems into "black box" approaches and causing uncertainty regarding the way they operate and, ultimately, the way that they come to decisions. This ambiguity has made it problematic for machine learning systems to be adopted in sensitive yet critical domains, where their value could be immense, such as healthcare. As a result, scientific interest in the field of Explainable Artificial Inte

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2. Evaluating machine learning-based intrusion detection systems with explainable AI: enhancing transparency and interpretability

   Vincent Zibi Mohale, Ibidun Christiana Obagbuwa · 2025 · Frontiers in Computer Science · 137 citations

   Machine Learning (ML)-based Intrusion Detection Systems (IDS) are integral to securing modern IoT networks but often suffer from a lack of transparency, functioning as “black boxes” with opaque decision-making processes. This study enhances IDS by integrating Explainable Artificial Intelligence (XAI), improving interpretability and trustworthiness while maintaining high predictive performance. Using the UNSW-NB15 dataset, comprising over 2.5 million records and nine diverse attack types, we developed and evaluated multiple ML models, including Decision Trees, Multilayer Perceptron (MLP), XGBoost, Random Forest, CatBoost, Logistic Regression, and Gaussian Naive Bayes. By incorporating XAI tec

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3. Explainable AI and Interpretable Machine Learning: A Case Study in Perspective

   Varad Vishwarupe, Prachi Manoj Joshi, Nicole Mathias, et al. · 2022 · Procedia Computer Science · 110 citations

   Explainable AI, as the word implies is a type of artificial intelligence which enables the explanation of learning models and focuses on why the system arrived at a particular decision, exploring its logical paradigms, contrary to the inherent black box nature of artificial intelligence. Similarly, machine learning interpretability allows users to comprehend the results of the learning models by providing reasoning for the decisions that it has arrived at. This nature of Explainable AI(XAI) and Interpretable Machine Learning (IML) is particularly helpful in the context of AI applications pertaining to healthcare and medical diagnosis. In this paper, we present a case study wherein we have fo

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4. Fostering trust and interpretability: integrating explainable AI (XAI) with machine learning for enhanced disease prediction and decision transparency

   Renuka Agrawal, Tawishi Gupta, Shaurya Gupta, et al. · 2025 · Diagnostic Pathology · 64 citations

   Medical healthcare has advanced substantially due to advancements in Artificial Intelligence (AI) techniques for early disease detection alongside support for clinical decisions. However, a gap exists in widespread adoption of results of these algorithms by public due to black box nature of models. The undisclosed nature of these systems creates fundamental obstacles within medical sectors that handle crucial cases because medical practitioners needs to understand the reasoning behind the outcome of a particular disease. A hybrid Machine Learning (ML) framework integrating Explainable AI (XAI) strategies that will improve both predictive performance and interpretability is explored in propos

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5. Explainable AI-driven depression detection from social media using natural language processing and black box machine learning models

   Sidra Hameed, M. Nauman, Nadeem Akhtar, et al. · 2025 · Frontiers in Artificial Intelligence · 43 citations

   Introduction Mental disorders are highly prevalent in modern society, leading to substantial personal and societal burdens. Among these, depression is one of the most common, often exacerbated by socioeconomic, clinical, and individual risk factors. With the rise of social media, user-generated content offers valuable opportunities for the early detection of mental disorders through computational approaches. Methods This study explores the early detection of depression using black-box machine learning (ML) models, including Support Vector Machines (SVM), Random Forests (RF), Extreme Gradient Boosting (XGB), and Artificial Neural Networks (ANN). Advanced Natural Language Processing (NLP) tech

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6. The role of explainable AI in enhancing breast cancer diagnosis using machine learning and deep learning models

   Z. A. Ansari, M. M. Tripathi, Rafeeq Ahmed · 2025 · Discover Artificial Intelligence · 42 citations

   Breast cancer is still a big health issue around the world, and it needs to be found quickly and perfectly to improve patient outcomes and lower death rates. Although artificial intelligence (AI) has showed amazing promise in breast cancer prediction mainly machine learning (ML) algorithms as well as deep learning (DL), practical use of these models is greatly hampered by their lack of interpretability and transparency. By giving complicated AI models interpretability, explainable artificial intelligence (XAI) becomes an essential tool to improve trust and transparency. XAI's efficacy in clinical environments is yet perfectly unidentified however, and its proper implementation into breast ca

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7. An explainable AI-based hybrid machine learning model for interpretability and enhanced crop yield prediction

   Anuradha Yenkikar, V. P. Mishra, Manish Bali, et al. · 2025 · MethodsX · 41 citations

   Agriculture is a major contributor to India's GDP and employs a large population. Key crops like rice are essential for food security, making higher yields crucial for sustainability. The use of machine learning (ML) in crop yield prediction has significantly improved forecast accuracy. However, the adoption of these models by policymakers and farmers is hindered by their lack of interpretability. Explainable Artificial Intelligence (XAI) techniques address this challenge by making AI-driven predictions more transparent, ensuring trust and better decision-making. This research integrates XAI techniques into a hybrid model that combines the powers of Random Forest (RF), Long Short-Term Memory

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8. Quantitative evaluation of uncertainty and interpretability in machine learning-based landslide susceptibility mapping through feature selection and explainable AI

   Xuan-Hien Le, Chanul Choi, Eu Song, et al. · 2024 · Frontiers in Environmental Science · 30 citations

   Landslide susceptibility mapping (LSM) is essential for determining risk regions and guiding mitigation strategies. Machine learning (ML) techniques have been broadly utilized, but the uncertainty and interpretability of these models have not been well-studied. This study conducted a comparative analysis and uncertainty assessment of five ML algorithms—Random Forest (RF), Light Gradient-Boosting Machine (LGB), Extreme Gradient Boosting (XGB), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM)—for LSM in Inje area, South Korea. We optimized these models using Bayesian optimization, a method that refines model performance through probabilistic model-based tuning of hyperparameters. The

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9. Explainable AI for Parkinson's disease prediction: A machine learning approach with interpretable models.

   A. Esan, D. Olawade, A. Soladoye, et al. · 2025 · Current research in translational medicine · 27 citations

   BACKGROUND Parkinson's Disease (PD) is a chronic, progressive neurological disorder with significant clinical and economic impacts globally. Early and accurate prediction remains challenging with traditional diagnostic methods due to subjectivity, delayed diagnosis, and variability. Machine Learning (ML) approaches offer potential solutions, yet their clinical adoption is hindered by limited interpretability. This study aimed to develop an interpretable ML model for early and accurate PD prediction using comprehensive multimodal datasets and Explainable Artificial Intelligence (XAI) techniques. METHODS The study applied five ML algorithms: Support Vector Machine (SVM), K-Nearest Neighbors

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10. An Interpretable and Generalizable Machine Learning Model for Predicting Asthma Outcomes: Integrating AutoML and Explainable AI Techniques

    Salman Mahmood, Raza Hasan, Saqib Hussain, et al. · 2025 · World · 24 citations

    Asthma remains a prevalent chronic condition, impacting millions globally and presenting significant clinical and economic challenges. This study develops a predictive model for asthma outcomes, leveraging automated machine learning (AutoML) and explainable AI (XAI) to balance high predictive accuracy with interpretability. Using a comprehensive dataset of demographic, clinical, and respiratory function data, we employed AutoGluon to automate model selection, optimization, and ensembling, resulting in a model with 98.99% accuracy and a 0.9996 ROC-AUC score. SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations) were applied to provide both global and

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11. Explainable AI for enhanced accuracy in malaria diagnosis using ensemble machine learning models

    Olushina Olawale Awe Awe, Peter Njoroge Mwangi, Samuel Kotva Goudoungou, et al. · 2025 · BMC Medical Informatics and Decision Making · 24 citations

    Malaria, an infectious disease caused by protozoan parasites belonging to the Plasmodium genus, remains a significant public health challenge, with African regions bearing the heaviest burden. Machine learning techniques have shown great promise in improving the diagnosis of infectious diseases, such as malaria. This study aims to integrate ensemble machine learning models and Explainable Artificial Intelligence (XAI) frameworks to enhance the diagnosis accuracy of malaria. The study utilized a dataset from the Federal Polytechnic Ilaro Medical Centre, Ilaro, Ogun State, Nigeria, which includes information from 337 patients aged between 3 and 77 years (180 females and 157 males) over a 4-wee

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12. A Comparative Study of Ensemble Machine Learning and Explainable AI for Predicting Harmful Algal Blooms

    O. Mermer, E. Zhang, I. Demir · 2025 · Big Data Cogn. Comput. · 23 citations

    Harmful algal blooms (HABs), driven by environmental pollution, pose significant threats to water quality, public health, and aquatic ecosystems. This study enhances the prediction of HABs in Lake Erie, part of the Great Lakes system, by utilizing ensemble machine learning (ML) models coupled with explainable artificial intelligence (XAI) for interpretability. Using water quality data from 2013 to 2020, various physical, chemical, and biological parameters were analyzed to predict chlorophyll-a (Chl-a) concentrations, which are a commonly used indicator of phytoplankton biomass and a proxy for algal blooms. This study employed multiple ensemble ML models, including random forest (RF), deep f

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13. Optimizing Stroke Recognition With MediaPipe and Machine Learning: An Explainable AI Approach for Facial Landmark Analysis

    Reshad Ul Karim, Sammam Mahdi, Abrar Samin, et al. · 2025 · IEEE Access · 21 citations

    Early detection of stroke is critical for improving survival rates and recovery. This study presents an innovative approach to stroke diagnosis through the analysis of facial landmarks combining MediaPipe’s facial landmark detection with advanced machine learning models’ Random Forest (RF), Extreme Gradient Boosting (XGB), and Categorical Boosting (CB). A comprehensive dataset of stroke and non-stroke facial images are curated, with MediaPipe extracting 228 facial landmarks from key regions affected by stroke-related asymmetry. Explainable AI (XAI) techniques are applied to enhance model interpretability, allowing for a deeper understanding of the most significant facial regions contributing

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14. Explainable AI in Healthcare: Leveraging Machine Learning and Knowledge Representation for Personalized Treatment Recommendations

    Md Shafiqul Islam, Mia Md. Tofayel Gonee Manik, M. Moniruzzaman, et al. · 2025 · Journal of Posthumanism · 15 citations

    In this research, an advanced framework is presented which combines Explainable Artificial Intelligence (XAI), machine learning algorithms and knowledge representation techniques to improve personalized treatment recommendations in healthcare. Random Forest, XGBoost and Deep Neural Networks (DNN) are used in this study to predict optimal treatment plans; thereby, SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations) provides means of explaining models. A method is implemented, which uses knowledge graphs and SNOMED CT and UMLS ontologies for structuring patient data and disease-treatment relationships. Thus, the proposed framework is trained and test

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15. Financial Statement Fraud Detection Through an Integrated Machine Learning and Explainable AI Framework

    Tsolmon Sodnomdavaa, Gunjargal Lkhagvadorj · 2025 · Journal of Risk and Financial Management · 14 citations

    Financial statement fraud remains a substantial risk in environments marked by weak regulatory oversight and information asymmetry. This study develops a decision-centric framework that integrates machine learning, explainable artificial intelligence, and decision curve analysis to improve fraud detection under severe class imbalance. Using 969 firm-year observations from 132 Mongolian firms (2013–2024), we evaluate 21 financial ratios with models including Random Forest, XGBoost, LightGBM, MLP, TabNet, and a Stacking Ensemble trained with SMOTE and class-weighted learning. Performance was assessed using PR-AUC, F1-score, Recall, and DeLong-based significance testing. The Stacking Ensemble a

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16. Explainable AI: Enhancing Interpretability of Machine Learning Models

    Duru Kulaklıoğlu · 2024 · Human computer interaction. · 12 citations

    Explainable Artificial Intelligence (XAI) is emerging as a critical field to address the “black box” nature of many machine learning (ML) models. While these models achieve high predictive accuracy, their opacity undermines trust, adoption, and ethical compliance in critical domains such as healthcare, finance, and autonomous systems. This research explores methodologies and frameworks to enhance the interpretability of ML models, focusing on techniques like feature attribution, surrogate models, and counterfactual explanations. By balancing model complexity and transparency, this study highlights strategies to bridge the gap between performance and explainability. The integration of XAI int

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17. Enhancing Cybersecurity in Internet of Vehicles: A Machine Learning Approach with Explainable AI for Real-Time Threat Detection

    Tanish Patel, Rutvij H. Jhaveri, Dhavalkumar Thakker, et al. · 2025 · Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing · 12 citations

    The proliferation of IoV technologies has revolutionized the use of transport systems to a great level of improvement in safety and efficiency, and convenience to users. On the other hand, increased connectivity has also brought new vulnerabilities, making IoV networks susceptible to a wide range of cyber-attacks. The contribution of this paper is the in-depth study of the development and evaluation of advanced machine learning (ML) models that detect and classify network anomalies in IoV ecosystems. Several classification models have been studied in our research to achieve high accuracy for discriminating between benign and malicious traffic. This work further harnesses Explainable AI (XAI)

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18. Advancement in Explainable AI: Bringing Transparency and Interpretability to Machine Learning Models for Use in High-Stakes Decisions

    Rajesh David, Hari Shankar, Prashanth Kura, et al. · 2025 · 2025 International Conference on Emerging Smart Computing and Informatics (ESCI) · 11 citations

    AI and ML have rapidly altered healthcare, finance, and criminal justice. These models are being used for high-stakes decision-making, raising questions about their openness and interpretability. Explainable AI (XAI), a subfield of machine learning that makes models more intelligible to users, is advancing in key applications where decisions can have a major influence on individuals and society. We begin by discussing black-box models' core issues, which can cause distrust and ethical issues by hiding the decision-making process. We show how model transparency has improved by analysing modern methods including model-agnostic methods, interpretable models, and visualisation tools. LIME (Local

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19. Comparative Study of Crop Yield Prediction Using Explainable AI and Interpretable Machine Learning Techniques

    Himanshu Pant, Garima Joshi, Bhupesh Rawat, et al. · 2025 · 2025 Fifth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) · 11 citations

    Predicting crop yields is a vital aspect of agricultural planning as well as food security. For this, machine-learning models have become quite effective tools. Using a real-world dataset centered on agricultural production predictions, this research evaluates the interpretability and predictive outcomes of four renowned machine learning models: XGBoost, Decision Tree, Random Forest, and Linear Regression. Key indicators of performance involving Mean Absolute Error (MAE), R-squared (R2), and Root Mean Squared Error (RMSE) have been employed to gauge the models' ability to deliver precise predictions. XGBoost Regression consistently performed better than the other models, as shown by its lowe

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20. A hybrid framework of statistical, machine learning, and explainable AI methods for school dropout prediction

    M. R. Khatun, M. Mim, M. Tasin, et al. · 2025 · PLOS One · 10 citations

    Student dropout is a significant challenge in Bangladesh, with serious impacts on both educational and socio-economic outcomes. This study investigates the factors influencing school dropout among students aged 6–24 years, employing data from the 2019 Multiple Indicator Cluster Survey (MICS). The research integrates statistical analysis with machine learning (ML) techniques and explainable AI (XAI) to identify key predictors and enhance model interpretability. Initially, descriptive and inferential statistical analyses were applied to identify significant predictors and guide feature selection. The hybrid feature selection strategy, combining statistical significance and model-based importan

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21. Machine Learning and Explainable AI for Liver Disease Prediction: An Integrated Interpretability Framework

    Niaz Ashraf Khan, Md. Ferdous Bin Hafiz, Md. Aktaruzzaman Pramanik, et al. · 2025 · Biomedical Materials & Devices · 6 citations

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22. Advancements in Explainable AI: Bridging the Gap Between Interpretability and Performance in Machine Learning Models

    Prof. Ashish Verma · 2025 · International Journal of Machine Learning, AI & Data Science Evolution · 2 citations

    The growing adoption of Artificial Intelligence (AI) and Machine Learning (ML) in critical decision-making areas such as healthcare, finance, and autonomous systems has raised concerns regarding the interpretability of these models. While deep learning and other advanced ML models deliver high accuracy, their "black box" nature makes it difficult to explain their decision-making processes. Explainable AI (XAI) aims to bridge this gap by introducing methods that enhance transparency without significantly compromising performance. This paper explores key advancements in XAI, including model-agnostic and model-specific interpretability techniques, and evaluates their effectiveness in balancing

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23. Explainable AI in Healthcare: Enhancing Trust through Interpretable Machine Learning Models

    Dr. Sudarsan Biswas · 2025 · International Journal of Machine Learning, AI & Data Science Evolution · 2 citations

    As artificial intelligence continues to reshape the healthcare industry, a growing concern among professionals and patients is the "black-box" nature of many machine learning models. While accuracy remains important, trust in AI decisions is equally vital, especially in critical areas like diagnosis and treatment planning. This paper explores the role of Explainable Artificial Intelligence (XAI) in building that trust by making machine learning outputs more transparent and understandable. Using real-world datasets and a case study in cardiovascular disease prediction, we evaluate how interpretable models and explanation techniques like SHAP and LIME improve clinician acceptance and decision-

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24. Improving the Interpretability of Asset Pricing Models by Explainable AI: A Machine Learning-based Approach

    FERRARA Massimiliano, CIANO Tiziana · 2024 · ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH · 2 citations

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