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Journal articles on the topic 'Algorithm explainability'

Author: Grafiati

Published: 1 June 2024

Last updated: 31 July 2025

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1

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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Kabir, Sami, Mohammad Shahadat Hossain, and Karl Andersson. "An Advanced Explainable Belief Rule-Based Framework to Predict the Energy Consumption of Buildings." Energies 17, no. 8 (2024): 1797. http://dx.doi.org/10.3390/en17081797.

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The prediction of building energy consumption is beneficial to utility companies, users, and facility managers to reduce energy waste. However, due to various drawbacks of prediction algorithms, such as, non-transparent output, ad hoc explanation by post hoc tools, low accuracy, and the inability to deal with data uncertainties, such prediction has limited applicability in this domain. As a result, domain knowledge-based explainability with high accuracy is critical for making energy predictions trustworthy. Motivated by this, we propose an advanced explainable Belief Rule-Based Expert System

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Xie, Lijie, Zhaoming Hu, Xingjuan Cai, Wensheng Zhang, and Jinjun Chen. "Explainable recommendation based on knowledge graph and multi-objective optimization." Complex & Intelligent Systems 7, no. 3 (2021): 1241–52. http://dx.doi.org/10.1007/s40747-021-00315-y.

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AbstractRecommendation system is a technology that can mine user's preference for items. Explainable recommendation is to produce recommendations for target users and give reasons at the same time to reveal reasons for recommendations. The explainability of recommendations that can improve the transparency of recommendations and the probability of users choosing the recommended items. The merits about explainability of recommendations are obvious, but it is not enough to focus solely on explainability of recommendations in field of explainable recommendations. Therefore, it is essential to con

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Yuxin Chen. "When Algorithms Testify: Addressing the Explainability Gap of AI Evidence in Criminal Cases." Studies in Law and Justice 4, no. 3 (2025): 1–10. https://doi.org/10.56397/slj.2025.06.01.

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The expansion of generative artificial intelligence evidence in the field of criminal justice has exposed the structural risks caused by the unexplainability of algorithms. Although existing studies have revealed multiple obstacles, they have not yet touched upon the fundamental crux of the unexplainability of the algorithm. The three predicaments derived from this, namely the disruption of argumentative logic, the loss of focus in the cross-examination process, and the depletion of judicial trust, essentially stem from the subtle tension between the certainty of machine conclusions and their

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Gräßer, Felix, Hagen Malberg, and Sebastian Zaunseder. "Neighborhood Optimization for Therapy Decision Support." Current Directions in Biomedical Engineering 5, no. 1 (2019): 1–4. http://dx.doi.org/10.1515/cdbme-2019-0001.

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AbstractThis work targets the development of a neighborhood-based Collaborative Filtering therapy recommender system for clinical decision support. The proposed algorithm estimates outcome of pharmaceutical therapy options in order to derive recommendations. Two approaches, namely a Relief-based algorithm and a metric learning approach are investigated. Both adapt similarity functions to the underlying data in order to determine the neighborhood incorporated into the filtering process. The implemented approaches are evaluated regarding the accuracy of the outcome estimations. The metric learni

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Maddala, Suresh Kumar. "Understanding Explainability in Enterprise AI Models." International Journal of Management Technology 12, no. 1 (2025): 58–68. https://doi.org/10.37745/ijmt.2013/vol12n25868.

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This article examines the critical role of explainability in enterprise AI deployments, where algorithmic transparency has emerged as both a regulatory necessity and a business imperative. As organizations increasingly rely on sophisticated machine learning models for consequential decisions, the "black box" problem threatens stakeholder trust, regulatory compliance, and effective model governance. We explore the multifaceted business case for explainable AI across regulated industries, analyze the spectrum of interpretability techniques—from inherently transparent models to post-hoc explanati

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Bulitko, Vadim, Shuwei Wang, Justin Stevens, and Levi H. S. Lelis. "Portability and Explainability of Synthesized Formula-based Heuristics." Proceedings of the International Symposium on Combinatorial Search 15, no. 1 (2022): 29–37. http://dx.doi.org/10.1609/socs.v15i1.21749.

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Heuristic search is a key component of automated planning and pathfinding. It is guided by a heuristic function which estimates remaining solution cost. Traditionally heuristic functions for pathfinding have been human-designed or pre-computed for a specific search graph. The former tend to be compact, human-readable but generic. The latter offer better guidance but require per-graph pre-computation and have a substantial memory cost. We aim to retain compactness and readability of human-designed heuristics and increase their performance. We adopt the recently published approach of representin

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García-Barceló, Carmen, David Gil, David Tomás, and David Bernabeu. "Prediction of Metastasis in Paragangliomas and Pheochromocytomas Using Machine Learning Models: Explainability Challenges." Sensors 25, no. 13 (2025): 4184. https://doi.org/10.3390/s25134184.

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One of the main issues with paragangliomas and pheochromocytomas is that these tumors have up to a 20% rate of metastatic disease, which cannot be reliably predicted. While machine learning models hold great promise for enhancing predictive accuracy, their often opaque nature limits trust and adoption in critical fields such as healthcare. Understanding the factors driving predictions is essential not only for validating their reliability but also for enabling their integration into clinical decision-making. In this paper, we propose an architecture that combines data mining, machine learning,

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Lv, Ge, and Lei Chen. "On Data-Aware Global Explainability of Graph Neural Networks." Proceedings of the VLDB Endowment 16, no. 11 (2023): 3447–60. http://dx.doi.org/10.14778/3611479.3611538.

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Graph Neural Networks (GNNs) have significantly boosted the performance of many graph-based applications, yet they serve as black-box models. To understand how GNNs make decisions, explainability techniques have been extensively studied. While the majority of existing methods focus on local explainability, we propose DAG-Explainer in this work aiming for global explainability. Specifically, we observe three properties of superior explanations for a pretrained GNN: they should be highly recognized by the model, compliant with the data distribution and discriminative among all the classes. The f

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Monsarrat, Paul, David Bernard, Mathieu Marty, et al. "Systemic Periodontal Risk Score Using an Innovative Machine Learning Strategy: An Observational Study." Journal of Personalized Medicine 12, no. 2 (2022): 217. http://dx.doi.org/10.3390/jpm12020217.

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Early diagnosis is crucial for individuals who are susceptible to tooth-supporting tissue diseases (e.g., periodontitis) that may lead to tooth loss, so as to prevent systemic implications and maintain quality of life. The aim of this study was to propose a personalized explainable machine learning algorithm, solely based on non-invasive predictors that can easily be collected in a clinic, to identify subjects at risk of developing periodontal diseases. To this end, the individual data and periodontal health of 532 subjects was assessed. A machine learning pipeline combining a feature selectio

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Kottinger, Justin, Shaull Almagor, and Morteza Lahijanian. "Conflict-Based Search for Explainable Multi-Agent Path Finding." Proceedings of the International Conference on Automated Planning and Scheduling 32 (June 13, 2022): 692–700. http://dx.doi.org/10.1609/icaps.v32i1.19859.

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The goal of the Multi-Agent Path Finding (MAPF) problem is to find non-colliding paths for agents in an environment, such that each agent reaches its goal from its initial location. In safety-critical applications, a human supervisor may want to verify that the plan is indeed collision-free. To this end, a recent work introduces a notion of explainability for MAPF based on a visualization of the plan as a short sequence of images representing time segments, where in each time segment the trajectories of the agents are disjoint. Then, the problem of Explainable MAPF via Segmentation asks for a

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Li, Tong, Jiale Deng, Yanyan Shen, Luyu Qiu, Huang Yongxiang, and Caleb Chen Cao. "Towards Fine-Grained Explainability for Heterogeneous Graph Neural Network." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (2023): 8640–47. http://dx.doi.org/10.1609/aaai.v37i7.26040.

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Heterogeneous graph neural networks (HGNs) are prominent approaches to node classification tasks on heterogeneous graphs. Despite the superior performance, insights about the predictions made from HGNs are obscure to humans. Existing explainability techniques are mainly proposed for GNNs on homogeneous graphs. They focus on highlighting salient graph objects to the predictions whereas the problem of how these objects affect the predictions remains unsolved. Given heterogeneous graphs with complex structures and rich semantics, it is imperative that salient objects can be accompanied with their

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Kong, Weihao, Jianping Chen, and Pengfei Zhu. "Machine Learning-Based Uranium Prospectivity Mapping and Model Explainability Research." Minerals 14, no. 2 (2024): 128. http://dx.doi.org/10.3390/min14020128.

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Sandstone-hosted uranium deposits are indeed significant sources of uranium resources globally. They are typically found in sedimentary basins and have been extensively explored and exploited in various countries. They play a significant role in meeting global uranium demand and are considered important resources for nuclear energy production. Erlian Basin, as one of the sedimentary basins in northern China, is known for its uranium mineralization hosted within sandstone formations. In this research, machine learning (ML) methodology was applied to mineral prospectivity mapping (MPM) of the me

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Öter, Ali, and Betül Ersöz. "Artificial Intelligence Assisted Solar Energy Forecasting by Explainability Approaches with LIME and SHAP." El-Cezeri Fen ve Mühendislik Dergisi 12, no. 2 (2025): 205–12. https://doi.org/10.31202/ecjse.1591721.

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Integrating renewable energy sources with new technologies such as artificial intelligence (AI) is important to balance energy supply and demand. The predictability of variable energy sources, such as solar energy, plays an important role in maintaining the stability and efficiency of power grids. This study examines the use of various algorithms in AI applications within renewable energy systems. The study critically evaluates existing methods and proposes an innovative approach for AI prediction in solar energy systems using advanced machine learning techniques. It focuses on the effectivene

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Karamanou, Areti, Petros Brimos, Evangelos Kalampokis, and Konstantinos Tarabanis. "Explainable Graph Neural Networks: An Application to Open Statistics Knowledge Graphs for Estimating House Prices." Technologies 12, no. 8 (2024): 128. http://dx.doi.org/10.3390/technologies12080128.

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In the rapidly evolving field of real estate economics, the prediction of house prices continues to be a complex challenge, intricately tied to a multitude of socio-economic factors. Traditional predictive models often overlook spatial interdependencies that significantly influence housing prices. The objective of this study is to leverage Graph Neural Networks (GNNs) on open statistics knowledge graphs to model these spatial dependencies and predict house prices across Scotland’s 2011 data zones. The methodology involves retrieving integrated statistical indicators from the official Scottish

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Huang, Xuanxiang, Yacine Izza, and Joao Marques-Silva. "Solving Explainability Queries with Quantification: The Case of Feature Relevancy." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (2023): 3996–4006. http://dx.doi.org/10.1609/aaai.v37i4.25514.

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Trustable explanations of machine learning (ML) models are vital in high-risk uses of artificial intelligence (AI). Apart from the computation of trustable explanations, a number of explainability queries have been identified and studied in recent work. Some of these queries involve solving quantification problems, either in propositional or in more expressive logics. This paper investigates one of these quantification problems, namely the feature relevancy problem (FRP), i.e.\ to decide whether a (possibly sensitive) feature can occur in some explanation of a prediction. In contrast with earl

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Fauvel, Kevin, Tao Lin, Véronique Masson, Élisa Fromont, and Alexandre Termier. "XCM: An Explainable Convolutional Neural Network for Multivariate Time Series Classification." Mathematics 9, no. 23 (2021): 3137. http://dx.doi.org/10.3390/math9233137.

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Multivariate Time Series (MTS) classification has gained importance over the past decade with the increase in the number of temporal datasets in multiple domains. The current state-of-the-art MTS classifier is a heavyweight deep learning approach, which outperforms the second-best MTS classifier only on large datasets. Moreover, this deep learning approach cannot provide faithful explanations as it relies on post hoc model-agnostic explainability methods, which could prevent its use in numerous applications. In this paper, we present XCM, an eXplainable Convolutional neural network for MTS cla

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Patel, Sagar, Sangeetha Abdu Jyothi, and Nina Narodytska. "CrystalBox: Future-Based Explanations for Input-Driven Deep RL Systems." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 13 (2024): 14563–71. http://dx.doi.org/10.1609/aaai.v38i13.29372.

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We present CrystalBox, a novel, model-agnostic, posthoc explainability framework for Deep Reinforcement Learning (DRL) controllers in the large family of input-driven environments which includes computer systems. We combine the natural decomposability of reward functions in input-driven environments with the explanatory power of decomposed returns. We propose an efficient algorithm to generate future-based explanations across both discrete and continuous control environments. Using applications such as adaptive bitrate streaming and congestion control, we demonstrate CrystalBox's capability to

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Tsiami, Lydia, and Christos Makropoulos. "Cyber—Physical Attack Detection in Water Distribution Systems with Temporal Graph Convolutional Neural Networks." Water 13, no. 9 (2021): 1247. http://dx.doi.org/10.3390/w13091247.

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Prompt detection of cyber–physical attacks (CPAs) on a water distribution system (WDS) is critical to avoid irreversible damage to the network infrastructure and disruption of water services. However, the complex interdependencies of the water network’s components make CPA detection challenging. To better capture the spatiotemporal dimensions of these interdependencies, we represented the WDS as a mathematical graph and approached the problem by utilizing graph neural networks. We presented an online, one-stage, prediction-based algorithm that implements the temporal graph convolutional networ

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Arous, Ines, Ljiljana Dolamic, Jie Yang, Akansha Bhardwaj, Giuseppe Cuccu, and Philippe Cudré-Mauroux. "MARTA: Leveraging Human Rationales for Explainable Text Classification." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 7 (2021): 5868–76. http://dx.doi.org/10.1609/aaai.v35i7.16734.

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Explainability is a key requirement for text classification in many application domains ranging from sentiment analysis to medical diagnosis or legal reviews. Existing methods often rely on "attention" mechanisms for explaining classification results by estimating the relative importance of input units. However, recent studies have shown that such mechanisms tend to mis-identify irrelevant input units in their explanation. In this work, we propose a hybrid human-AI approach that incorporates human rationales into attention-based text classification models to improve the explainability of class

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Botana, Iñigo López-Riobóo, Carlos Eiras-Franco, and Amparo Alonso-Betanzos. "Regression Tree Based Explanation for Anomaly Detection Algorithm." Proceedings 54, no. 1 (2020): 7. http://dx.doi.org/10.3390/proceedings2020054007.

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This work presents EADMNC (Explainable Anomaly Detection on Mixed Numerical and Categorical spaces), a novel approach to address explanation using an anomaly detection algorithm, ADMNC, which provides accurate detections on mixed numerical and categorical input spaces. Our improved algorithm leverages the formulation of the ADMNC model to offer pre-hoc explainability based on CART (Classification and Regression Trees). The explanation is presented as a segmentation of the input data into homogeneous groups that can be described with a few variables, offering supervisors novel information for j

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Wajdi, Wajdi. "Computer Aided Brain Tumor Diagnosis using Coati Optimization Algorithm with Explainable Artificial Intelligence Approach." Fusion: Practice and Applications 17, no. 2 (2025): 24–37. http://dx.doi.org/10.54216/fpa.170203.

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Brain tumors (BT) are a difficult and dangerous medical condition, and the accurate and early analysis of these tumors is crucial for suitable treatment. Explainability in clinical image diagnosis role a vital play in the correct analysis and treatment of tumors that supports medical staff's optimum understanding of the image analysis performances rely upon deep methods. Artificial intelligence (AI), in certain deep neural networks (DNNs) has attained remarkable outcomes for clinical image analysis in many applications. However, the need for explainability of deep neural approaches has been as

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Lv, Ting, Zhenkuan Pan, Weibo Wei, et al. "Iterative deep neural networks based on proximal gradient descent for image restoration." PLOS ONE 17, no. 11 (2022): e0276373. http://dx.doi.org/10.1371/journal.pone.0276373.

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The algorithm unfolding networks with explainability of algorithms and higher efficiency of Deep Neural Networks (DNN) have received considerable attention in solving ill-posed inverse problems. Under the algorithm unfolding network framework, we propose a novel end-to-end iterative deep neural network and its fast network for image restoration. The first one is designed making use of proximal gradient descent algorithm of variational models, which consists of denoiser and reconstruction sub-networks. The second one is its accelerated version with momentum factors. For sub-network of denoiser,

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Gao, Jingyue, Xiting Wang, Yasha Wang, and Xing Xie. "Explainable Recommendation through Attentive Multi-View Learning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3622–29. http://dx.doi.org/10.1609/aaai.v33i01.33013622.

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Recommender systems have been playing an increasingly important role in our daily life due to the explosive growth of information. Accuracy and explainability are two core aspects when we evaluate a recommendation model and have become one of the fundamental trade-offs in machine learning. In this paper, we propose to alleviate the trade-off between accuracy and explainability by developing an explainable deep model that combines the advantages of deep learning-based models and existing explainable methods. The basic idea is to build an initial network based on an explainable deep hierarchy (e

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Banditwattanawong, Thepparit, and Masawee Masdisornchote. "On Characterization of Norm-Referenced Achievement Grading Schemes toward Explainability and Selectability." Applied Computational Intelligence and Soft Computing 2021 (February 18, 2021): 1–14. http://dx.doi.org/10.1155/2021/8899649.

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Grading is the process of interpreting learning competence to inform learners and instructors of the current learning ability levels and necessary improvement. For norm-referenced grading, the instructors use a conventionally statistical method, z score. It is difficult for such a method to achieve explainable grade discrimination to resolve dispute between learners and instructors. To solve such difficulty, this paper proposes a simple and efficient algorithm for explainable norm-referenced grading. Moreover, the rise of artificial intelligence nowadays makes machine learning techniques attra

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Chatterjee, Soumick, Arnab Das, Chirag Mandal, et al. "TorchEsegeta: Framework for Interpretability and Explainability of Image-Based Deep Learning Models." Applied Sciences 12, no. 4 (2022): 1834. http://dx.doi.org/10.3390/app12041834.

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Clinicians are often very sceptical about applying automatic image processing approaches, especially deep learning-based methods, in practice. One main reason for this is the black-box nature of these approaches and the inherent problem of missing insights of the automatically derived decisions. In order to increase trust in these methods, this paper presents approaches that help to interpret and explain the results of deep learning algorithms by depicting the anatomical areas that influence the decision of the algorithm most. Moreover, this research presents a unified framework, TorchEsegeta,

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Ben-Efraim, Hadar, Susan B. Davidson, and Amit Somech. "SHARQ: Explainability Framework for Association Rules on Relational Data." Proceedings of the ACM on Management of Data 3, no. 1 (2025): 1–25. https://doi.org/10.1145/3709726.

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Association rules are an important technique for gaining insights over large relational datasets consisting of tuples of elements (i.e. attribute-value pairs). However, it is difficult to explain the relative importance of data elements with respect to the rules in which they appear. This paper develops a measure of an element's contribution to a set of association rules based on Shapley values, denoted SHARQ (ShApley Rules Quantification). As is the case with many Shapely-based computations, the cost of a naive calculation of the score is exponential in the number of elements. To that end, we

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Isakov, A. O., N. F. Gusarova, D. A. Dobrenko, and A. A. Golubev. "Explainability of Agent Behavior in Clinical Decision Support Systems." Economics Law Innovaion, no. 4 (December 29, 2024): 50–59. https://doi.org/10.17586/2713-1874-2024-4-50-59.

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The article considers the task of improving the explainability of agent behavior in clinical decision support systems using multi-agent reinforcement learning, taking into account the behavioral characteristics of individual agents. Special attention is paid to the application of the proximal policy optimization (PPO) algorithm, which is used to simulate the interaction of artificial and natural intelligence agents. In addition, the importance of taking into account the behavior-al characteristics of patients is considered, which is achieved through the use of the developed framework «beliefs-

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Rudzite, Liva. "Algorithmic Explainability and the Sufficient-Disclosure Requirement under the European Patent Convention." Juridica International 31 (October 25, 2022): 125–35. http://dx.doi.org/10.12697/ji.2022.31.09.

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Artificial intelligence and its subsector machine learning differs from traditional programming. For this reason, coupled with its potential benefits to society in many arenas, it has been articulated as one of the key priorities in the European Union. Such characteristics specific to artificial intelligence as models with increased accuracy and generalisation power may accentuate issues of algorithmic explainability that can defy patentability. Accordingly, the article focuses on the legal requirements related to the ‘sufficient disclosure’ criterion under the legal framework for patents as o

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Krishna Adithya, Venkatesh, Bryan M. Williams, Silvester Czanner, et al. "EffUnet-SpaGen: An Efficient and Spatial Generative Approach to Glaucoma Detection." Journal of Imaging 7, no. 6 (2021): 92. http://dx.doi.org/10.3390/jimaging7060092.

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Current research in automated disease detection focuses on making algorithms “slimmer” reducing the need for large training datasets and accelerating recalibration for new data while achieving high accuracy. The development of slimmer models has become a hot research topic in medical imaging. In this work, we develop a two-phase model for glaucoma detection, identifying and exploiting a redundancy in fundus image data relating particularly to the geometry. We propose a novel algorithm for the cup and disc segmentation “EffUnet” with an efficient convolution block and combine this with an exten

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Lizzi, Francesca, Camilla Scapicchio, Francesco Laruina, Alessandra Retico, and Maria Evelina Fantacci. "Convolutional Neural Networks for Breast Density Classification: Performance and Explanation Insights." Applied Sciences 12, no. 1 (2021): 148. http://dx.doi.org/10.3390/app12010148.

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We propose and evaluate a procedure for the explainability of a breast density deep learning based classifier. A total of 1662 mammography exams labeled according to the BI-RADS categories of breast density was used. We built a residual Convolutional Neural Network, trained it and studied the responses of the model to input changes, such as different distributions of class labels in training and test sets and suitable image pre-processing. The aim was to identify the steps of the analysis with a relevant impact on the classifier performance and on the model explainability. We used the grad-CAM

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Adithyaram, N. "Early Detection of Lung Disease Using Deep Learning Algorithms on Image Data." International Journal for Research in Applied Science and Engineering Technology 11, no. 7 (2023): 466–69. http://dx.doi.org/10.22214/ijraset.2023.53802.

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Abstract: This research paper presents a deep learning-based algorithm for the early detection of lung diseases using medical image data. The algorithm demonstrates high accuracy, sensitivity, specificity, precision, and AUC-ROC values, outperforming existing methods. By leveraging deep learning techniques, the algorithm provides a valuable tool for accurate disease identification, enabling timely interventions and improving patient outcomes. The study discusses the algorithm's performance, generalizability, and clinical relevance, highlighting its potential impact on clinical practice. Future

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Fang, Xue, Lin Li, and Zheng Wei. "Design of Recommendation Algorithm Based on Knowledge Graph." Journal of Physics: Conference Series 2425, no. 1 (2023): 012025. http://dx.doi.org/10.1088/1742-6596/2425/1/012025.

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Abstract The commodity data in the shopping system contains a wealth of feature information, and different commodities are related by these features. Traditional collaborative filtering algorithms represent commodity data in a structured way, but they have issues such as low commodity similarity calculation accuracy, poor recommendation effect, and unfriendly recommendation results. The commodity recommendation algorithm based on the knowledge graph put forward in this paper initially automatically extracts the entities and entity relationships in the commodity data as the vertices and edges o

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Samaras, Agorastos-Dimitrios, Serafeim Moustakidis, Ioannis D. Apostolopoulos, Elpiniki Papageorgiou, and Nikolaos Papandrianos. "Uncovering the Black Box of Coronary Artery Disease Diagnosis: The Significance of Explainability in Predictive Models." Applied Sciences 13, no. 14 (2023): 8120. http://dx.doi.org/10.3390/app13148120.

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In recent times, coronary artery disease (CAD) prediction and diagnosis have been the subject of many Medical decision support systems (MDSS) that make use of machine learning (ML) and deep learning (DL) algorithms. The common ground of most of these applications is that they function as black boxes. They reach a conclusion/diagnosis using multiple features as input; however, the user is oftentimes oblivious to the prediction process and the feature weights leading to the eventual prediction. The primary objective of this study is to enhance the transparency and comprehensibility of a black-bo

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Shalev, Yuval, and Irad Ben-Gal. "Context Based Predictive Information." Entropy 21, no. 7 (2019): 645. http://dx.doi.org/10.3390/e21070645.

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We propose a new algorithm called the context-based predictive information (CBPI) for estimating the predictive information (PI) between time series, by utilizing a lossy compression algorithm. The advantage of this approach over existing methods resides in the case of sparse predictive information (SPI) conditions, where the ratio between the number of informative sequences to uninformative sequences is small. It is shown that the CBPI achieves a better PI estimation than benchmark methods by ignoring uninformative sequences while improving explainability by identifying the informative sequen

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Negro, Pablo Ariel, and Claudia Pons. "Rule Extraction in Trained Feedforward Deep Neural Networks." International Journal of Artificial Intelligence and Machine Learning 13, no. 1 (2024): 1–22. http://dx.doi.org/10.4018/ijaiml.347988.

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Explainability is a key aspect of machine learning, necessary for ensuring transparency and trust in decision-making processes. As machine learning models become more complex, the integration of neural and symbolic approaches has emerged as a promising solution to the explainability problem. One effective solution involves using search techniques to extract rules from trained deep neural networks by examining weight and bias values and calculating their correlation with outputs. This article proposes incorporating cosine similarity in this process to narrow down the search space and identify t

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Silva-Aravena, Fabián, Hugo Núñez Delafuente, Jimmy H. Gutiérrez-Bahamondes, and Jenny Morales. "A Hybrid Algorithm of ML and XAI to Prevent Breast Cancer: A Strategy to Support Decision Making." Cancers 15, no. 9 (2023): 2443. http://dx.doi.org/10.3390/cancers15092443.

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Worldwide, the coronavirus has intensified the management problems of health services, significantly harming patients. Some of the most affected processes have been cancer patients’ prevention, diagnosis, and treatment. Breast cancer is the most affected, with more than 20 million cases and at least 10 million deaths by 2020. Various studies have been carried out to support the management of this disease globally. This paper presents a decision support strategy for health teams based on machine learning (ML) tools and explainability algorithms (XAI). The main methodological contributions are:

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Satoni Kurniawansyah, Arius. "EXPLAINABLE ARTIFICIAL INTELLIGENCE THEORY IN DECISION MAKING TREATMENT OF ARITHMIA PATIENTS WITH USING DEEP LEARNING MODELS." Jurnal Rekayasa Sistem Informasi dan Teknologi 1, no. 1 (2022): 26–41. http://dx.doi.org/10.59407/jrsit.v1i1.75.

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In the context of Explainable Artificial Intelligence, there are two important keywords: interpretability and "explainability". Interpretability is the extent to which humans can understand the causes of decisions. The better the interpretability of an AI/ML model, the easier it is for someone to understand why certain decisions or predictions have been made. Some cases of AI/ML implementation may not require explanation, because they are used in a low-risk environment, meaning mistakes will not have serious consequences. The need for interpretability and explainability arises when an AI syste

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Vranay, Dominik, Maroš Hliboký, László Kovács, and Peter Sinčák. "Using Segmentation to Boost Classification Performance and Explainability in CapsNets." Machine Learning and Knowledge Extraction 6, no. 3 (2024): 1439–65. http://dx.doi.org/10.3390/make6030068.

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In this paper, we present Combined-CapsNet (C-CapsNet), a novel approach aimed at enhancing the performance and explainability of Capsule Neural Networks (CapsNets) in image classification tasks. Our method involves the integration of segmentation masks as reconstruction targets within the CapsNet architecture. This integration helps in better feature extraction by focusing on significant image parts while reducing the number of parameters required for accurate classification. C-CapsNet combines principles from Efficient-CapsNet and the original CapsNet, introducing several novel improvements

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Sattarbek, Alisher, Bekzat Zhumashev, and Serikzhan Parmanov. "EXPLORING THE IMPACT OF MACHINE LEARNING ON KYCCOMPLIANCE COSTS AND CUSTOMER EXPERIENCE." Suleyman Demirel University Bulletin Natural and Technical Sciences 63, no. 2 (2024): 24–30. https://doi.org/10.47344/sdubnts.v63i2.976.

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The Know Your Customer (KYC) compliance process is a critical requirement for financial institutions to prevent money laundering, fraud, and terrorist financing. Machine learning algorithms have the potential to improve the efficiency and accuracy of KYC compliance checks. In this study, we explored the effectiveness of several classification algorithms for KYCcompliance checks using a dataset with 3000 rows collected from a famousbanking system in Kazakhstan. We compared the performance of fourcommonly used algorithms: Decision Tree, Random Forest, LogisticRegression, and Support Vector Machi

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BUITEN, Miriam C. "Towards Intelligent Regulation of Artificial Intelligence." European Journal of Risk Regulation 10, no. 1 (2019): 41–59. http://dx.doi.org/10.1017/err.2019.8.

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Artificial intelligence (AI) is becoming a part of our daily lives at a fast pace, offering myriad benefits for society. At the same time, there is concern about the unpredictability and uncontrollability of AI. In response, legislators and scholars call for more transparency and explainability of AI. This article considers what it would mean to require transparency of AI. It advocates looking beyond the opaque concept of AI, focusing on the concrete risks and biases of its underlying technology: machine-learning algorithms. The article discusses the biases that algorithms may produce through

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