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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.

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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 · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · 46,039 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

  7. Towards A Rigorous Science of Interpretable Machine Learning

    Finale Doshi‐Velez, Been Kim · 2017 · arXiv (Cornell University) · 3,179 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

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

    Pantelis Linardatos, Vasilis Papastefanopoulos, Sotiris Kotsiantis · 2020 · Entropy · 2,855 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

  9. Machine learning and deep learning

    Christian Janiesch, Patrick Zschech, Kai Heinrich · 2021 · Electronic Markets · 2,566 citations

    Abstract Today, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of systems to learn from problem-specific training data to automate the process of analytical model building and solve associated tasks. Deep learning is a machine learning concept based on artificial neural networks. For many applications, deep learning models outperform shallow machine learning models and traditional data analysis approaches. In this article, we summarize the fundamentals of machine learning and deep learning to generate a broader understanding of the methodical underpinning of current intelligent systems. In particular

  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 · 2,140 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

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

    Vikas Hassija, Vinay Chamola, Atmesh Mahapatra, et al. · 2023 · Cognitive Computation · 1,907 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

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

    Varad Vishwarupe, Prachi M. Joshi, Nicole Mathias, et al. · 2022 · Procedia Computer Science · 106 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

  13. 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 · 103 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

  16. Explainable AI: Enhancing Interpretability of Machine Learning Models

    Duru Kulaklıoğlu · 2024 · Human Computer Interaction · 8 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

  18. 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 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

  19. 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

    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

  20. 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

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

    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

  22. 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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