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Research papers on Machine learning interpretability

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

    Pantelis Linardatos, Vasilis Papastefanopoulos, Sotiris B. Kotsiantis · 2020 · Entropy · 2,907 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 · 141 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 · 111 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 · 48 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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  6. 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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  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 · 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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  9. 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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  11. Explainable AI: Interpreting and Understanding Machine Learning Models

    Elisha Blessing · 2024 · HAL (Le Centre pour la Communication Scientifique Directe)

    Explainable Artificial Intelligence (AI) has emerged as a field of study that aims to provide transparency and interpretability to machine learning models. As AI algorithms become increasingly complex and pervasive in various domains, the ability to understand and interpret their decisions becomes crucial for ensuring fairness, accountability, and trustworthiness. This abstract provides an overview of the importance of explainable AI and highlights some of the key techniques and approaches used in interpreting and understanding machine learning models. The abstract begins by emphasizing the growing significance of explainability in AI systems. As machine learning models are deployed in criti

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  12. Transparency and Interpretability in Cloudbased Machine Learning with Explainable AI

    AAMC, Washington, D.C., USA, Dhruvitkumar V. Talati · 2024 · International Journal of Multidisciplinary Research in Science, Engineering and Technology.

    With the increased complexity of machine learning models and their widespread use in cloud applications, interpretability and transparency of decision-making are the highest priority. Explainable AI (XAI) methods seek to shed light on the inner workings of machine learning models, hence making them more interpretable and enabling users to rely on them. In this article, we explain the importance of XAI in cloud-computer environments, specifically with regards to having interpretable models and explainable decision-making. [1] XAI is the essence of a paradigm shift in cloud-based ML, promoting transparency, accountability, and ethical decision-making. As cloudbased ML keeps becoming mainstream

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  13. The Rise of Explainable AI: Trends in Interpretability for Machine Learning Models

    T. John Paul Antony, V. Kamakshi, B. Anandapriya · 2024 · Nanotechnology Perceptions

    The increasing deployment of machine learning (ML) models across critical domains such as healthcare, finance, and autonomous systems has highlighted the urgent need for transparency and accountability in AI decision-making. This has spurred a growing focus on explainable AI (XAI), a subfield dedicated to developing methods and tools that render complex ML models interpretable to humans. This paper explores the key trends in XAI, examining both the theoretical foundations and practical approaches used to enhance the interpretability of machine learning models. We provide a comprehensive review of recent advancements in interpretable model design, post-hoc explainability techniques, and evalu

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  17. Explainable AI Bridging the Gap Between Machine Learning Models and Human Interpretability

    Vishesh Narendra Pamadi, Daksha Borada · 2025 · Journal of Quantum Science and Technology

    Explainable AI (XAI) is an emerging field that seeks to bridge the gap between the transparent, at times impenetrable decision-making that is a natural consequence of machine learning (ML) models and human understanding. With artificial intelligence solutions gaining increasing prominence, especially in high-stakes markets like healthcare, finance, and law enforcement, the need for such models to be comprehensible, transparent, and trustworthy has become a major challenge. Despite significant advancements in AI technologies, the "black-box" nature of many models, especially those with deep learning and reinforcement learning, makes them unsuitable to implement in practical applications where

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  20. Explainable xgboost framework for multi-class disease severity prediction: a clinical machine learning study with shap-based interpretability

    Gouse Baig Mohammad · 2025 · Journal of Artificial Intelligence Machine Learning and Neural Network

    Non-communicable diseases (NCDs) are a major growing problem, and responsible for around 74% of all deaths globally. Early and correct stratification of the severity of the disease is essential for timely therapeutic interventions, efficient use of clinical resources and patient benefits. Traditional Clinical Scoring Systems (CSSs) like APACHE II and SOFA are based on handcrafted and limited features and miss the complex and non-linear interactions between variables in multi-morbid patients. In this study, a novel and interpretable machine learning (ML) pipeline is developed based on extreme gradient boosting (XGBoost) model for four class disease severity prediction (Mild, Moderate, Severe,

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