Relevant bibliographies by topics / Explanability

Contents

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

Academic literature on the topic 'Explanability'

Author: Grafiati
Published: 27 July 2024
Last updated: 31 July 2025

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Journal articles on the topic "Explanability"

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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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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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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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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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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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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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Dissertations / Theses on the topic "Explanability"

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Bertrand, Astrid. "Misplaced trust in AI : the explanation paradox and the human-centric path. A characterisation of the cognitive challenges to appropriately trust algorithmic decisions and applications in the financial sector." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAT012.

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L'IA devenant de plus en plus présente dans nos vies, nous sommes soucieux de comprendrele fonctionnement de ces structures opaques. Pour répondre à cette demande, le domaine de la recherche en explicabilité (XAI) s'est considérablement développé au cours des dernières années. Cependant, peu de travaux ont étudié le besoin en explicabilité des régulateurs ou des consommateurs à la lumière d'exigences légales en matière d'explications. Cette thèse s'attache à comprendre le rôle des explications pour permettre la conformité réglementaire des systèmes améliorés par l'IA dans des applications fina
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Book chapters on the topic "Explanability"

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Daglarli, Evren. "Explainable Artificial Intelligence (xAI) Approaches and Deep Meta-Learning Models for Cyber-Physical Systems." In Advances in Systems Analysis, Software Engineering, and High Performance Computing. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-5101-1.ch003.

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Today, the effects of promising technologies such as explainable artificial intelligence (xAI) and meta-learning (ML) on the internet of things (IoT) and the cyber-physical systems (CPS), which are important components of Industry 4.0, are increasingly intensified. However, there are important shortcomings that current deep learning models are currently inadequate. These artificial neural network based models are black box models that generalize the data transmitted to it and learn from the data. Therefore, the relational link between input and output is not observable. For these reasons, it i
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Conference papers on the topic "Explanability"

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Fayas Ahamed, F., M. Prasanth, Atul Saju Sundaresh, D. Manoj Krishna, and S. Sindhu. "Multimodal Hate Speech Detection With Explanability Using LIME." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724886.

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Reinfred Athevan, Gian, Felix Indra Kurniadi, Thomas Amarta Gunawisesa, Anderies, and Imanuel Tio. "Insurance Fraud Detection: A Perspective with S-LIME Explanability." In 2024 International Conference on Data Science and Its Applications (ICoDSA). IEEE, 2024. http://dx.doi.org/10.1109/icodsa62899.2024.10652155.

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Singla, Kushal, and Subham Biswas. "Machine learning explanability method for the multi-label classification model." In 2021 IEEE 15th International Conference on Semantic Computing (ICSC). IEEE, 2021. http://dx.doi.org/10.1109/icsc50631.2021.00063.

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Hampel-Arias, Zigfried, Adra Carr, Natalie Klein, and Eric Flynn. "2D Spectral Representations and Autoencoders for Hyperspectral Imagery Classification and ExplanabilitY." In 2024 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI). IEEE, 2024. http://dx.doi.org/10.1109/ssiai59505.2024.10508608.

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Montoya, Fernando, Esteban Berríos, Daniela Díaz, and Hernán Astudillo. "Counterfactual Explanability: An Application of Causal Inference in a Financial Sector Delivery Business Process." In 2023 42nd IEEE International Conference of the Chilean Computer Science Society (SCCC). IEEE, 2023. http://dx.doi.org/10.1109/sccc59417.2023.10315742.

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Panati, Chandana, Simon Wagner, and Stefan Brüggenwirth. "Multiple Target Recognition Within SAR Scene Achieved Using YOLO and Explanability Investigated Using Gradient-Free Visualisation." In 2024 IEEE Radar Conference (RadarConf24). IEEE, 2024. http://dx.doi.org/10.1109/radarconf2458775.2024.10548088.

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You might also be interested in the extended bibliographies on the topic 'Explanability' for particular source types:
Journal articles
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