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Papers on “machine learning interpretability explainable AI”

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

   Pantelis Linardatos, Vasilis Papastefanopoulos, Sotiris Kotsiantis · 2020 · Entropy · 2,600 cites

   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

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 · 122 cites

   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

3. 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 · 45 cites

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

   Anuradha Yenkikar, Ved Prakash Mishra, Manish Bali, et al. · 2025 · MethodsX · 35 cites

5. Explainable AI: Enhancing Interpretability of Machine Learning Models

   Duru Kulaklıoğlu · 2024 · Human Computer Interaction · 10 cites

   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

6. 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 cites

7. 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 · 1 cite

   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 bal

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

   Dr. Sudarsan Biswas · 2025 · International Journal of Machine Learning, AI & Data Science Evolution · 1 cite

   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