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Journal articles on the topic 'Explanability'

Author: Grafiati

Published: 27 July 2024

Last updated: 31 July 2025

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1

Collier, John. "Reduction, supervenience, and physical emergence." Behavioral and Brain Sciences 27, no. 5 (2004): 629–30. http://dx.doi.org/10.1017/s0140525x04240146.

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After distinguishing reductive explanability in principle from ontological deflation, I give a case of an obviously physical property that is reductively inexplicable in principle. I argue that biological systems often have this character, and that, if we make certain assumptions about the cohesion and dynamics of the mind and its physical substrate, then it is emergent according to Broad's criteria.

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2

Chinyere Christian, Emedo. "Explainability Imperative of Generative Artificial Intelligence Navigating the Moral Dilemma of AI in Nigeria and Charting a Path for the Future." Universal Library of Arts and Humanities 01, no. 02 (2024): 38–43. http://dx.doi.org/10.70315/uloap.ulahu.2024.0102007.

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This paper explores the explanability imperative in the context of Generative Artificial Intelligence (GAI) and its crucial role in addressing the concerns posed by AI technology in Nigeria. This underscores the ethical necessity for AI systems, especially generative ones to provide clear and understandable explanations for their decisions and actions. Although the advent of generative AI undoubtedly heralds the future and however, has also exposed Nigerian society to new vulnerabilities that seemingly are detrimental to our epistemic agency and peaceful political settings. Employing the pheno

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Hu, Hanqing, Mehmed Kantardzic, and Shreyas Kar. "Explainable data stream mining: Why the new models are better." Intelligent Decision Technologies 18, no. 1 (2024): 371–85. http://dx.doi.org/10.3233/idt-230065.

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Explainable Machine Learning brings expandability, interpretability, and accountability to Data Mining Algorithms. Existing explanation frameworks focus on explaining the decision process of a single model in a static dataset. However, in data stream mining changes in data distribution over time, called concept drift, may require updating the learning models to reflect the current data environment. It is therefore important to go beyond static models and understand what has changed among the learning models before and after a concept drift. We propose a Data Stream Explanability framework (DSE

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4

Venkata Krishnamoorthy, T., C. Venkataiah, Y. Mallikarjuna Rao, et al. "A novel NASNet model with LIME explanability for lung disease classification." Biomedical Signal Processing and Control 93 (July 2024): 106114. http://dx.doi.org/10.1016/j.bspc.2024.106114.

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5

BARAJAS ARANDA, DANIEL ALEJANDRO, MIGUEL ANGEL SICILIA URBAN, MARIA DOLORES TORRES SOTO, and AURORA TORRES SOTO. "COMPARISON AND EXPLANABILITY OF MACHINE LEARNING MODELS IN PREDICTIVE SUICIDE ANALYSIS." DYNA NEW TECHNOLOGIES 11, no. 1 (2024): [10P.]. http://dx.doi.org/10.6036/nt11028.

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ABSTRACT In this comparative study of machine learning models for predicting suicidal behavior, three approaches were evaluated: neural network, logistic regression, and decision trees. The results revealed that the neural network showed the best predictive performance, with an accuracy of 82.35%, followed by logistic regression (76.47%) and decision trees (64.71%). Additionally, the explainability analysis revealed that each model assigned different importance to the features in predicting suicidal behavior, highlighting the need to understand how models interpret features and how they influe

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Pachouly, Mrs Shikha J. "The Role of Explanability in AI-Driven Fashion Recommendation Model - A Review." International Journal for Research in Applied Science and Engineering Technology 12, no. 1 (2024): 769–75. http://dx.doi.org/10.22214/ijraset.2024.56885.

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Abstract: Fashion recommendation systems powered by AI have transformed the way consumers discover clothing and accessories. However, these systems often lack transparency, leaving users in the dark about why certain recommendations are made. This review paper explores "The Role of Explainability in AI-Driven Fashion Recommendation Models." We begin by establishing the fundamentals of AI-driven fashion recommendations and the challenges they face, such as subjective fashion preferences and the need to balance personalization and diversity. The paper also explores evaluation metrics for measuri

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Adam, Carole, Patrick Taillandier, Julie Dugdale, and Benoit Gaudou. "BDI vs FSM Agents in Social Simulations for Raising Awareness in Disasters." International Journal of Information Systems for Crisis Response and Management 9, no. 1 (2017): 27–44. http://dx.doi.org/10.4018/ijiscram.2017010103.

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Each summer in Australia, bushfires burn many hectares of forest, causing deaths, injuries, and destroying property. Agent-based simulation is a powerful tool to test various management strategies on a simulated population, and to raise awareness of the actual population behaviour. But valid results depend on realistic underlying models. This article describes two simulations of the Australian population's behaviour during bushfires designed in previous work, one based on a finite-state machine architecture, the other based on a belief-desire-intention agent architecture. It then proposes seve

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Oh, Gunung, Nakyung Shin, Suji Lee, Yoonjin Lee, Hohyun Jung, and Miyoung Hong. "Analysis and Prediction of R&D Investment Portfolio Based on Topic Modeling." Korean Data Analysis Society 26, no. 4 (2024): 993–1004. http://dx.doi.org/10.37727/jkdas.2024.26.4.993.

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Many countries are identifying crucial technologies for national security and economic growth amid global tech competition, crafting cross-departmental strategies to pursue them. We propose a platform for analyzing and predicting R&D investment portfolios based on government R&D project information and investment data to provide scientific evidence for strategic budget allocation. The proposed platform consists of the following four processes. First, topic modeling and project labeling are performed using project-related text data. Second, we propose a method for calculating the import

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9

Hollis, Kate Fultz, Lina F. Soualmia, and Brigitte Séroussi. "Artificial Intelligence in Health Informatics: Hype or Reality?" Yearbook of Medical Informatics 28, no. 01 (2019): 003–4. http://dx.doi.org/10.1055/s-0039-1677951.

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Objectives: To provide an introduction to the 2019 International Medical Informatics Association (IMIA) Yearbook by the editors. Methods: This editorial presents an overview and introduction to the 2019 IMIA Yearbook which includes the special topic “Artificial Intelligence in Health: New Opportunities, Challenges, and Practical Implications". The special topic is discussed, the IMIA President’s statement is introduced, and changes in the Yearbook editorial team are described. Results: Artificial intelligence (AI) in Medicine arose in the 1970’s from new approaches for representing expert know

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10

Tejaskumar Pujari, Anshul Goel, and Ashwin Sharma. "Ethical and Responsible AI: Governance Frameworks and Policy Implications for Multi-Agent Systems." International Journal Science and Technology 3, no. 1 (2024): 72–89. https://doi.org/10.56127/ijst.v3i1.1962.

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Semi-autonomous, augmented- Artificial Intelligence has become increasingly relevant as collective activities are practiced by two or more autonomic entities. MAS and AI at the intersection have fostered very new waves of socioeconomic exchange, necessitating technological governance and, the most challenging element of them all, ethical governance. These autonomous systems involve a network of decision-making agents working in a decentralized environment, entailing very high accountability, transparency, explanability, ethical alignment, and practically everything in between. The escalated so

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11

Hussain, Sardar Mehboob, Domenico Buongiorno, Nicola Altini, et al. "Shape-Based Breast Lesion Classification Using Digital Tomosynthesis Images: The Role of Explainable Artificial Intelligence." Applied Sciences 12, no. 12 (2022): 6230. http://dx.doi.org/10.3390/app12126230.

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Computer-aided diagnosis (CAD) systems can help radiologists in numerous medical tasks including classification and staging of the various diseases. The 3D tomosynthesis imaging technique adds value to the CAD systems in diagnosis and classification of the breast lesions. Several convolutional neural network (CNN) architectures have been proposed to classify the lesion shapes to the respective classes using a similar imaging method. However, not only is the black box nature of these CNN models questionable in the healthcare domain, but so is the morphological-based cancer classification, conce

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12

Cha, Hyunjung, and Hunsik Kang. "Comparison of Level and Relationship in Attitudes and Ethical Awareness toward Artificial Intelligence between Elementary General and Science-Gifted Students." Korean Science Education Society for the Gifted 16, no. 1 (2024): 50–61. http://dx.doi.org/10.29306/jseg.2024.16.1.50.

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This study compared the level and relationship in attitudes and ethical awareness toward Artificial Intelligence (AI) between elementary general and science-gifted students. For this purpose, 90 elementary general students in grades5-6 and 87 elementary science-gifted students in grades 5-6 were selected, and their attitudes toward AI and ethical awareness toward AI were tested. The results of the independent samples t-test showed that the means of the science-gifted students were statistically significantly higher than those of the general students in the overall and all sub-scales of attitud

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13

Kumar, Sowmya Ramesh, and Samarth Ramesh Kedilaya. "Navigating Complexity: Harnessing AI for Multivariate Time Series Forecasting in Dynamic Environments." Journal of Engineering and Applied Sciences Technology, December 31, 2023, 1–8. http://dx.doi.org/10.47363/jeast/2023(5)219.

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Multivariate Time Series Analysis (MTSA) plays a pivotal role in forecasting within diverse domains by addressing the complexities arising from interdependencies among multiple variables. This exploration delves into the fundamentals, methodologies, and applications of MTSA, elucidating its role in enhancing predictive capabilities. The key concepts in MTSA, including Vector Autoregression, Cointegration, Error Correction Models, and Granger Causality, form the foundation for understanding dynamic relationships among variables. The methodology section outlines the critical steps in MTSA, such

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14

Ullah, Waseem, Samee Ullah Khan, Min Je Kim, et al. "Industrial defective chips detection using deep convolutional neural network with inverse feature matching mechanism." Journal of Computational Design and Engineering, February 20, 2024. http://dx.doi.org/10.1093/jcde/qwae019.

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Abstract The growing demand for high-quality industrial products has led to a significant emphasis on image anomaly detection (AD). Anomaly detection in industrial goods presents a formidable research challenge that demands the application of sophisticated techniques to identify and address deviations from the expected norm accurately. Manufacturers increasingly recognize the significance of employing intelligent systems to detect flaws and defects in product parts. However, industrial settings pose several challenges: diverse categories, limited abnormal samples, and vagueness. Hence, there i

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15

Chatterjee, Swagato, and Meghraj Panmand. "Explaining and predicting click-baitiness and click-bait virality." Industrial Management & Data Systems, August 12, 2022. http://dx.doi.org/10.1108/imds-01-2022-0003.

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PurposeIn the age of social media, when publishers are vying for consumer attention, click-baits have become very common. Not only viral websites but also mainstream publishers, such as news channels, use click-baits for generating traffic. Therefore, click-bait detection and prediction of click-bait virality have become important challenges for social media platforms to keep the platform click-bait free and give a better user experience. The purpose of this study is to try exploring how the contents of the social media posts and the article can be used to explain and predict social media post

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16

Nguyen, Thu Trang, Thach Le Nguyen, and Georgiana Ifrim. "Robust explainer recommendation for time series classification." Data Mining and Knowledge Discovery, June 20, 2024. http://dx.doi.org/10.1007/s10618-024-01045-8.

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AbstractTime series classification is a task which deals with temporal sequences, a prevalent data type common in domains such as human activity recognition, sports analytics and general sensing. In this area, interest in explanability has been growing as explanation is key to understand the data and the model better. Recently, a great variety of techniques (e.g., LIME, SHAP, CAM) have been proposed and adapted for time series to provide explanation in the form of saliency maps, where the importance of each data point in the time series is quantified with a numerical value. However, the salien

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17

Upadhyay, Nitin, Shalini Upadhyay, Salma S. Abed, and Yogesh K. Dwivedi. "Consumer adoption of mobile payment services during COVID-19: extending meta-UTAUT with perceived severity and self-efficacy." International Journal of Bank Marketing, January 25, 2022. http://dx.doi.org/10.1108/ijbm-06-2021-0262.

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Purpose The purpose of this paper is to identify and examine the important factors that could affect consumers' behavioural intention and use behaviour towards mobile payment services during COVID-19. Design/methodology/approach The proposed model extends meta-Unified Theory of Acceptance and Use of Technology (meta-UTAUT) model with perceived severity and self-efficacy factors affecting consumers' behavioural intention and use behaviour towards mobile payment services. A convenient sampling technique has been utilized to gather data from a self-administered questionnaire. The data collection

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