Relevant bibliographies by topics / Algorithm explainability

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Algorithm explainability'

Author: Grafiati
Published: 1 June 2024
Last updated: 31 July 2025

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Journal articles on the topic "Algorithm explainability"

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Nuobu, Gengpan. "Transformer model: Explainability and prospectiveness." Applied and Computational Engineering 20, no. 1 (2023): 88–99. http://dx.doi.org/10.54254/2755-2721/20/20231079.

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The purpose of Artificial Intelligence(AI) is to simulate learning process of human brain by strong computing power and appropriate algorithm, so that the machine can develop judging ability at work as human. Current AI mainly relies on Deep Learning model which is based on artificial neural network, like Convolutional Neural Network(CNN) in computer visualization, but that also takes with some defects. This paper introduces defects of CNN and discusses Transformer model in solving unexplainability of traditional CNN algorithm. To discuss why the Transformer model and attention mechanism are c
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Cheng, Xueyi, and Chang Che. "Interpretable Machine Learning: Explainability in Algorithm Design." Journal of Industrial Engineering and Applied Science 2, no. 6 (2024): 65–70. https://doi.org/10.70393/6a69656173.323337.

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In recent years, there is a high demand for transparency and accountability in machine learning models, especially in domains such as healthcare, finance and etc. In this paper, we delve into deep how to make machine learning models more interpretable, with focus on the importance of the explainability of the algorithm design. The main objective of this paper is to fill this gap and provide a comprehensive survey and analytical study towards AutoML. To that end, we first introduce the AutoML technology and review its various tools and techniques.
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Hwang, Hyunseung, and Steven Euijong Whang. "XClusters: Explainability-First Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (2023): 7962–70. http://dx.doi.org/10.1609/aaai.v37i7.25963.

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We study the problem of explainability-first clustering where explainability becomes a first-class citizen for clustering. Previous clustering approaches use decision trees for explanation, but only after the clustering is completed. In contrast, our approach is to perform clustering and decision tree training holistically where the decision tree's performance and size also influence the clustering results. We assume the attributes for clustering and explaining are distinct, although this is not necessary. We observe that our problem is a monotonic optimization where the objective function is
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Pendyala, Vishnu, and Hyungkyun Kim. "Assessing the Reliability of Machine Learning Models Applied to the Mental Health Domain Using Explainable AI." Electronics 13, no. 6 (2024): 1025. http://dx.doi.org/10.3390/electronics13061025.

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Machine learning is increasingly and ubiquitously being used in the medical domain. Evaluation metrics like accuracy, precision, and recall may indicate the performance of the models but not necessarily the reliability of their outcomes. This paper assesses the effectiveness of a number of machine learning algorithms applied to an important dataset in the medical domain, specifically, mental health, by employing explainability methodologies. Using multiple machine learning algorithms and model explainability techniques, this work provides insights into the models’ workings to help determine th
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Mahmood, Alaa Mohammed, and İsa Avcı. "Cybersecurity Defence Mechanism Against DDoS Attack with Explainability." Mesopotamian Journal of CyberSecurity 4, no. 3 (2024): 278–90. https://doi.org/10.58496/mjcs/2024/027.

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Application-layer attacks (Layer 7 attacks), a form of distributed denial-of-service (DDoS) aimed at web servers, have become a significant concern in cybersecurity because of their ability to disrupt services by overwhelming server resources. This study focuses on addressing the challenges of detecting and mitigating the impact of such attacks, which are difficult to counter due to their sophisticated nature. The primary objective of this study is to develop an effective monitoring and defence model to detect, defend, and respond to these attacks efficiently. To achieve this, SHapley Additive
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Loreti, Daniela, and Giorgio Visani. "Parallel approaches for a decision tree-based explainability algorithm." Future Generation Computer Systems 158 (September 2024): 308–22. http://dx.doi.org/10.1016/j.future.2024.04.044.

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Yiğit, Tuncay, Nilgün Şengöz, Özlem Özmen, Jude Hemanth, and Ali Hakan Işık. "Diagnosis of Paratuberculosis in Histopathological Images Based on Explainable Artificial Intelligence and Deep Learning." Traitement du Signal 39, no. 3 (2022): 863–69. http://dx.doi.org/10.18280/ts.390311.

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Artificial intelligence holds great promise in medical imaging, especially histopathological imaging. However, artificial intelligence algorithms cannot fully explain the thought processes during decision-making. This situation has brought the problem of explainability, i.e., the black box problem, of artificial intelligence applications to the agenda: an algorithm simply responds without stating the reasons for the given images. To overcome the problem and improve the explainability, explainable artificial intelligence (XAI) has come to the fore, and piqued the interest of many researchers. A
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Wang, Zhenzhong, Qingyuan Zeng, Wanyu Lin, Min Jiang, and Kay Chen Tan. "Generating Diagnostic and Actionable Explanations for Fair Graph Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 19 (2024): 21690–98. http://dx.doi.org/10.1609/aaai.v38i19.30168.

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A plethora of fair graph neural networks (GNNs) have been proposed to promote algorithmic fairness for high-stake real-life contexts. Meanwhile, explainability is generally proposed to help machine learning practitioners debug models by providing human-understandable explanations. However, seldom work on explainability is made to generate explanations for fairness diagnosis in GNNs. From the explainability perspective, this paper explores the problem of what subgraph patterns cause the biased behavior of GNNs, and what actions could practitioners take to rectify the bias? By answering the two
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Powell, Alison B. "Explanations as governance? Investigating practices of explanation in algorithmic system design." European Journal of Communication 36, no. 4 (2021): 362–75. http://dx.doi.org/10.1177/02673231211028376.

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The algorithms underpinning many everyday communication processes are now complex enough that rendering them explainable has become a key governance objective. This article examines the question of 'who should be required to explain what, to whom, in platform environments'. By working with algorithm designers and using design methods to extrapolate existing capacities to explain aglorithmic functioning, the article discusses the power relationships underpinning explanation of algorithmic function. Reviewing how key concepts of transparency and accountability connect with explainability, the pa
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Wu, Jinrong, Su Nguyen, Thimal Kempitiya, and Damminda Alahakoon. "A Hierarchical Machine Learning Method for Detection and Visualization of Network Intrusions from Big Data." Technologies 12, no. 10 (2024): 204. http://dx.doi.org/10.3390/technologies12100204.

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Machine learning is regarded as an effective approach in network intrusion detection, and has gained significant attention in recent studies. However, few intrusion detection methods have been successfully applied to detect anomalies in large-scale network traffic data, and low explainability of the complex algorithms has caused concerns about fairness and accountability. A further problem is that many intrusion detection systems need to work with distributed data sources in the cloud. In this paper, we propose an intrusion detection method based on distributed computing to learn the latent re
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Dissertations / Theses on the topic "Algorithm explainability"

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Raizonville, Adrien. "Regulation and competition policy of the digital economy : essays in industrial organization." Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAT028.

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Cette thèse aborde deux enjeux auxquels les régulateurs doivent faire face dans l’économie numérique : le défi informationnel généré par l'utilisation de nouvelles technologies d'intelligence artificielle et la problématique du pouvoir de marché des grandes plateformes numériques. Le premier chapitre de cette thèse étudie la mise en place d’un système d’audit (coûteux et imparfait) par un régulateur cherchant à réduire le risque de dommage généré par les technologies d’intelligence artificielle, tout en limitant le coût de la régulation. Les entreprises peuvent investir dans l'explicabilité de
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Li, Honghao. "Interpretable biological network reconstruction from observational data." Electronic Thesis or Diss., Université Paris Cité, 2021. http://www.theses.fr/2021UNIP5207.

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Cette thèse porte sur les méthodes basées sur des contraintes. Nous présentons comme exemple l’algorithme PC, pour lequel nous proposons une modification qui garantit la cohérence des ensembles de séparation, utilisés pendant l’étape de reconstruction du squelette pour supprimer les arêtes entre les variables conditionnellement indépendantes, par rapport au graphe final. Elle consiste à itérer l’algorithme d’apprentissage de structure tout en limitant la recherche des ensembles de séparation à ceux qui sont cohérents par rapport au graphe obtenu à la fin de l’itération précédente. La contraint
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BODINI, MATTEO. "DESIGN AND EXPLAINABILITY OF MACHINE LEARNING ALGORITHMS FOR THE CLASSIFICATION OF CARDIAC ABNORMALITIES FROM ELECTROCARDIOGRAM SIGNALS." Doctoral thesis, Università degli Studi di Milano, 2022. http://hdl.handle.net/2434/888002.

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The research activity contained in the present thesis work is devoted to the development of novel Machine Learning (ML) and Deep Learning (DL) algorithms for the classification of Cardiac Abnormalities (CA) from Electrocardiogram (ECG) signals, along with the explanation of classification outputs with explainable approaches. Automated computer programs for ECG classification have been developed since 1950s to improve the correct interpretation of the ECG, nowadays facilitating health care decision-making by reducing costs and human errors. The first ECG interpretation computer programs were es
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Kong, Lanfang. "Explainable algorithms for anomaly detection and time series forecasting." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAT039.

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L'intelligence artificielle a montré des performances dominantes dans le domaine de l'exploration de données, avec des applications dans divers domaines, y compris des domaines critiques tels que la médecine, la finance, la justice, etc. En conséquence, l'explicabilité des modèles de boîte noire devient de plus en plus exigeante. Nous nous concentrons sur deux applications spécifiques : la détection d'anomalies et la prévision de séries chronologiques, et présentons XTREK et ADAPATCH pour chaque tâche, respectivement. XTREK est une approche non supervisée basée sur un arbre pour la détection d
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Radulovic, Nedeljko. "Post-hoc Explainable AI for Black Box Models on Tabular Data." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAT028.

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Les modèles d'intelligence artificielle (IA) actuels ont fait leurs preuves dans la résolution de diverses tâches, telles que la classification, la régression, le traitement du langage naturel (NLP) et le traitement d'images. Les ressources dont nous disposons aujourd'hui nous permettent d'entraîner des modèles d'IA très complexes pour résoudre différents problèmes dans presque tous les domaines : médecine, finance, justice, transport, prévisions, etc. Avec la popularité et l'utilisation généralisée des modèles d'IA, la nécessite d'assurer la confiance dans ces modèles s'est également accrue.
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Jeyasothy, Adulam. "Génération d'explications post-hoc personnalisées." Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS027.

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La thèse se place dans le domaine de l'IA explicable (XAI, eXplainable AI). Nous nous concentrons sur les méthodes d'interprétabilité post-hoc qui visent à expliquer à un utilisateur la prédiction pour une donnée d'intérêt spécifique effectuée par un modèle de décision entraîné. Pour augmenter l'interprétabilité des explications, cette thèse étudie l'intégration de connaissances utilisateur dans ces méthodes, et vise ainsi à améliorer la compréhensibilité de l'explication en générant des explications personnalisées adaptées à chaque utilisateur. Pour cela, nous proposons un formalisme général
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Book chapters on the topic "Algorithm explainability"

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Trace, Ciaran B., and James A. Hodges. "The Role of Paradata in Algorithmic Accountability." In Knowledge Management and Organizational Learning. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-53946-6_11.

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AbstractThis chapter examines how the doings of the algorithm (instantiated through its operations, actions, and steps) and its accompanying algorithmic system are revealed and explored through an engagement with the paradata created as a part of this data-making effort. In doing so, the chapter explores how the concept of paradata helps us understand how information professionals and domain stakeholders conceptualize accountable algorithmic entities and how this influences how they emerge as documented and describable entities. Two complementary frameworks for capturing and preserving paradat
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Bologna, Guido, Jean-Marc Boutay, Quentin Leblanc, and Damian Boquete. "Fidex: An Algorithm for the Explainability of Ensembles and SVMs." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-61137-7_35.

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Rady, Amgad, and Franck van Breugel. "Explainability of Probabilistic Bisimilarity Distances for Labelled Markov Chains." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30829-1_14.

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AbstractProbabilistic bisimilarity distances measure the similarity of behaviour of states of a labelled Markov chain. The smaller the distance between two states, the more alike they behave. Their distance is zero if and only if they are probabilistic bisimilar. Recently, algorithms have been developed that can compute probabilistic bisimilarity distances for labelled Markov chains with thousands of states within seconds. However, say we compute that the distance of two states is 0.125. How does one explain that 0.125 captures the similarity of their behaviour?In this paper, we address this q
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Wang, Huaduo, and Gopal Gupta. "FOLD-SE: An Efficient Rule-Based Machine Learning Algorithm with Scalable Explainability." In Practical Aspects of Declarative Languages. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-52038-9_3.

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Baniecki, Hubert, Wojciech Kretowicz, and Przemyslaw Biecek. "Fooling Partial Dependence via Data Poisoning." In Machine Learning and Knowledge Discovery in Databases. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26409-2_8.

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AbstractMany methods have been developed to understand complex predictive models and high expectations are placed on post-hoc model explainability. It turns out that such explanations are not robust nor trustworthy, and they can be fooled. This paper presents techniques for attacking Partial Dependence (plots, profiles, PDP), which are among the most popular methods of explaining any predictive model trained on tabular data. We showcase that PD can be manipulated in an adversarial manner, which is alarming, especially in financial or medical applications where auditability became a must-have t
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Huang, Yiran, Yexu Zhou, Haibin Zhao, Likun Fang, Till Riedel, and Michael Beigl. "ExTea: An Evolutionary Algorithm-Based Approach for Enhancing Explainability in Time-Series Models." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70381-2_27.

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Duke, Toju. "Explainability." In Building Responsible AI Algorithms. Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9306-5_7.

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Neubig, Stefan, Daria Cappey, Nicolas Gehring, Linus Göhl, Andreas Hein, and Helmut Krcmar. "Visualizing Explainable Touristic Recommendations: An Interactive Approach." In Information and Communication Technologies in Tourism 2024. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-58839-6_37.

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AbstractPersonalized recommendations have played a vital role in tourism, serving various purposes, ranging from an improved visitor experience to addressing sustainability issues. However, research shows that recommendations are more likely to be accepted by visitors if they are comprehensible and appeal to the visitors’ common sense. This highlights the importance of explainable recommendations that, according to a previously specified goal, explain an algorithm’s inference process, generate trust among visitors, or educate visitors by making them aware of sustainability practices. Based on
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Barzas, Konstantinos, Shereen Fouad, Gainer Jasa, and Gabriel Landini. "An Explainable Deep Learning Framework for Mandibular Canal Segmentation from Cone Beam Computed Tomography Volumes." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-82768-6_1.

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Abstract Cone Beam Computed Tomography (CBCT) is an indispensable imaging modality in oral radiology, offering comprehensive dental anatomical information. Accurate detection of the mandibular canal (MC), a crucial anatomical structure in the lower jaw, within CBCT volumes is essential to support clinical dentistry workflows, including diagnosis, preoperative treatment planning, and postoperative evaluation. In this study, we present a deep learning-based (DL) approach for MC segmentation using 3D U-Net and 3D Attention U-Net networks. We collected a unique dataset of CBCT scans from 20 anonym
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dos Anjos, Lucas Costa. "Rethinking Algorithmic Explainability Through the Lenses of Intellectual Property and Competition." In Information Technology and Law Series. T.M.C. Asser Press, 2024. https://doi.org/10.1007/978-94-6265-639-0_13.

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AbstractAlgorithmic decision-making is integral to digital platforms, influencing user experiences and societal dynamics. This paper chapter scrutinizes algorithmic opacity, highlighting the inherent biases, the anti-competitive strategies that may result from dominant market power and the potential for discrimination within these systems. Despite the promise of objectivity, algorithms often operate under a veil of opacity, shaping content and information access, with significant implications for individual perspectives and societal functioning. The chapter explores the legal challenges posed
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Conference papers on the topic "Algorithm explainability"

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Zafaranchi, Arman, Francesca Lizzi, Alessandra Retico, Camilla Scapicchio, and Maria Fantacci. "Explainability Applied to a Deep-Learning Based Algorithm for Lung Nodule Segmentation." In 1st International Conference on Explainable AI for Neural and Symbolic Methods. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0013014600003886.

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Sofo, Michelangelo, Giuseppe Labianca, Giancarlo Mauri, and Francesco Combierati. "System DietadHoc: A Fusion of Human-Centered Design and Agile Development for the Explainability of AI Techniques Based on Clinical and Nutritional Data." In 16th International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013054800003911.

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Izza, Yacine, Xuanxiang Huang, Antonio Morgado, Jordi Planes, Alexey Ignatiev, and Joao Marques-Silva. "Distance-Restricted Explanations: Theoretical Underpinnings & Efficient Implementation." In 21st International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/kr.2024/45.

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The uses of machine learning (ML) have snowballed in recent years. In many cases, ML models are highly complex, and their operation is beyond the understanding of human decision-makers. Nevertheless, some uses of ML models involve high-stakes and safety-critical applications. Explainable artificial intelligence (XAI) aims to help human decision-makers in understanding the operation of such complex ML models, thus eliciting trust in their operation. Unfortunately, the majority of past XAI work is based on informal approaches, that offer no guarantees of rigor. Unsurprisingly, there exists compr
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Gull, Carlos Quintero, Jose Aguilar, and Rodrigo García. "Study of Explainability Analysis Methods for the LAMDA Family Algorithms in Classification and Clustering Tasks." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651500.

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Boccuzzi, Giannangelo, Alberto Nico, and Flavio Manganello. "HARMONIZING HUMAN AND ALGORITHMIC ASSESSMENT: LEGAL REFLECTIONS ON THE RIGHT TO EXPLAINABILITY IN EDUCATION." In 17th International Conference on Education and New Learning Technologies. IATED, 2025. https://doi.org/10.21125/edulearn.2025.2446.

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Zhang, Tongze, Tammy Chung, Anind Dey, and Sang Won Bae. "Exploring Algorithmic Explainability: Generating Explainable AI Insights for Personalized Clinical Decision Support Focused on Cannabis Intoxication in Young Adults." In 2024 International Conference on Activity and Behavior Computing (ABC). IEEE, 2024. http://dx.doi.org/10.1109/abc61795.2024.10652070.

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Parbat, Shreyas, Isabell Viedt, and Leon Urbas. "A Comparative Evaluation of Complexity in Mechanistic and Surrogate Modeling Approaches for Digital Twins." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.122855.

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A Digital Twin (DT) is a purposeful digital representation of a physical entity that employs data, algorithms, and software to enhance operations, making it possible to e.g., forecast failures, or evaluate new designs through the simulation of real-world scenarios. DTs are enablers for real-time monitoring, simulation, and optimization. However, traditional simulation DTs often rely on complex, non-linear mechanistic models with high computational demands, complex structures, and a large number of specific parameters and thus pose quite a challenge to maintainability. Surrogate models, on the
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Zhou, Tongyu, Haoyu Sheng, and Iris Howley. "Assessing Post-hoc Explainability of the BKT Algorithm." In AIES '20: AAAI/ACM Conference on AI, Ethics, and Society. ACM, 2020. http://dx.doi.org/10.1145/3375627.3375856.

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Mollel, Rachel Stephen, Lina Stankovic, and Vladimir Stankovic. "Using explainability tools to inform NILM algorithm performance." In BuildSys '22: The 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. ACM, 2022. http://dx.doi.org/10.1145/3563357.3566148.

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Haid, Charlotte, Gia-phong Tran, and Johannes Fottner. "Explainability in AI-based shift scheduling." In 2025 Intelligent Human Systems Integration. AHFE International, 2025. https://doi.org/10.54941/ahfe1005831.

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As digitalization becomes more widely used in factories, preference-based shift planning is evolving into an important tool for human-centered work in various workplaces. Human-centered shift planning not only increases the efficiency of work, but above all considers the individual's preferences for certain shifts or activities and thereby empowers human workers. However, the planning algorithm used in previous work is based on AI and the algorithm is not able to explain why certain decisions in scheduling were made. The aim of this publication is to use AI-based shift scheduling as an example
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