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Research papers on Machine learning interpretability

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  1. AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at Scale

    Kefallinos, Dionysios, Alexandris, Georgios, Maras, Alexis, et al. · 2018 · arXiv (Cornell University) · 46,029 citations

    Although geospatial question answering systems have received increasing attention in recent years, existing prototype systems struggle to properly answer qualitative spatial questions. In this work, we propose a unique framework for answering qualitative spatial questions, which comprises three main components: a geoparser that takes the input questions and extracts place semantic information from text, a reasoning system which is embedded with a crisp reasoner, and finally, answer extraction, which refines the solution space and generates final answers. We present an experimental design to evaluate our framework for point-based cardinal direction calculus (CDC) relations by developing an au

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  7. Towards A Rigorous Science of Interpretable Machine Learning

    Finale Doshi‐Velez, Been Kim · 2017 · arXiv (Cornell University) · 3,195 citations

    As machine learning systems become ubiquitous, there has been a surge of interest in interpretable machine learning: systems that provide explanation for their outputs. These explanations are often used to qualitatively assess other criteria such as safety or non-discrimination. However, despite the interest in interpretability, there is very little consensus on what interpretable machine learning is and how it should be measured. In this position paper, we first define interpretability and describe when interpretability is needed (and when it is not). Next, we suggest a taxonomy for rigorous evaluation and expose open questions towards a more rigorous science of interpretable machine learni

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

    Pantelis Linardatos, Vasilis Papastefanopoulos, S. Kotsiantis · 2020 · Entropy · 2,924 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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  9. 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 · 2,622 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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  10. Definitions, methods, and applications in interpretable machine learning

    William J. Murdoch, Chandan Singh, Karl Kumbier, et al. · 2019 · Proceedings of the National Academy of Sciences · 2,171 citations

    Machine-learning models have demonstrated great success in learning complex patterns that enable them to make predictions about unobserved data. In addition to using models for prediction, the ability to interpret what a model has learned is receiving an increasing amount of attention. However, this increased focus has led to considerable confusion about the notion of interpretability. In particular, it is unclear how the wide array of proposed interpretation methods are related and what common concepts can be used to evaluate them. We aim to address these concerns by defining interpretability in the context of machine learning and introducing the predictive, descriptive, relevant (PDR) fram

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  11. Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence

    Vikas Hassija, Vinay Chamola, Atmesh Mahapatra, et al. · 2023 · Cognitive Computation · 2,012 citations

    Abstract Recent years have seen a tremendous growth in Artificial Intelligence (AI)-based methodological development in a broad range of domains. In this rapidly evolving field, large number of methods are being reported using machine learning (ML) and Deep Learning (DL) models. Majority of these models are inherently complex and lacks explanations of the decision making process causing these models to be termed as 'Black-Box'. One of the major bottlenecks to adopt such models in mission-critical application domains, such as banking, e-commerce, healthcare, and public services and safety, is the difficulty in interpreting them. Due to the rapid proleferation of these AI models, explaining th

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

    Vincent Zibi Mohale, I. Obagbuwa · 2025 · Frontiers Comput. Sci. · 122 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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  13. Explainable AI and Interpretable Machine Learning: A Case Study in Perspective

    Varad Vishwarupe, Prachi M. Joshi, Nicole Mathias, et al. · 2022 · Procedia Computer Science · 109 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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  14. 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 · 55 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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  15. 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 · 38 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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  16. 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 · 35 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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  17. 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 · 28 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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  18. 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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  19. 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 · 22 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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  20. 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 · 22 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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  21. 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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  22. 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. · 21 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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  23. 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 · 13 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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  24. 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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