Relevant bibliographies by topics / Explainability generative AI

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Academic literature on the topic 'Explainability generative AI'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Explainability generative AI"

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Genevieve Okafor, Ehisuoria E. Akhuemonkhan, Chibuzor Njoku, Evelyn Gachui, Ifeoma Naibe, and Aniel K. Diala. "The future of generative artificial intelligence (AI) in fraud detection analysis." International Journal of Management & Entrepreneurship Research 7, no. 4 (2025): 294–98. https://doi.org/10.51594/ijmer.v7i4.1875.

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As financial fraud schemes grow increasingly sophisticated, traditional detection models struggle to keep pace with the evolving threat landscape. Generative Artificial Intelligence (AI), particularly models like Generative Adversarial Networks (GANs) and Large Language Models (LLMs), are emerging as transformative tools in the realm of fraud detection. These models enable the creation of synthetic datasets, simulate fraudulent behaviors, and enhance the accuracy of anomaly detection systems. By generating realistic fraud scenarios, generative AI enhances predictive modeling and supports proac
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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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Kang, Hyunju, Geonhee Han, Yoonjae Jeong, and Hogun Park. "AudioGenX: Explainability on Text-to-Audio Generative Models." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 17 (2025): 17733–41. https://doi.org/10.1609/aaai.v39i17.33950.

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Text-to-audio generation models (TAG) have achieved significant advances in generating audio conditioned on text descriptions. However, a critical challenge lies in the lack of transparency regarding how each textual input impacts the generated audio. To address this issue, we introduce AudioGenX, an Explainable AI (XAI) method that provides explanations for text-to-audio generation models by highlighting the importance of input tokens. AudioGenX optimizes an Explainer by leveraging factual and counterfactual objective functions to provide faithful explanations at the audio token level. This m
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Srilekha Kanakadandi. "Leveraging Generative AI in Telecom E-commerce: A Framework for Enhanced Development and Testing Optimization." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 2525–33. https://doi.org/10.32628/cseit251112231.

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This article investigates the integration of generative AI technologies within telecom e-commerce platform development and testing workflows. By examining real-world implementations across multiple organizations, the research provides insights into how AI-driven approaches enhance code generation, test coverage, and API optimization in microservices architectures. The article explores the implementation of AI tools within existing CI/CD pipelines, focusing on automated test case generation, dynamic data creation, and intelligent debugging processes. Particular attention is given to security co
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Dehal, Ramandeep Singh, Mehak Sharma, and Enayat Rajabi. "Knowledge Graphs and Their Reciprocal Relationship with Large Language Models." Machine Learning and Knowledge Extraction 7, no. 2 (2025): 38. https://doi.org/10.3390/make7020038.

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The reciprocal relationship between Large Language Models (LLMs) and Knowledge Graphs (KGs) highlights their synergistic potential in enhancing artificial intelligence (AI) applications. LLMs, with their natural language understanding and generative capabilities, support the automation of KG construction through entity recognition, relation extraction, and schema generation. Conversely, KGs serve as structured and interpretable data sources that improve the transparency, factual consistency and reliability of LLM-based applications, mitigating challenges such as hallucinations and lack of expl
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Researcher. "THE CRITICAL ROLE OF DATA ENGINEERING IN GENERATIVE AI." International Journal of Computer Engineering and Technology (IJCET) 15, no. 4 (2024): 829–40. https://doi.org/10.5281/zenodo.13475740.

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This article explores the intricate relationship between data engineering and generative AI (Gen AI), highlighting the critical role that data engineering plays in the development, deployment, and optimization of Gen AI systems. It delves into the nature of generative AI and its revolutionary capabilities across various domains, from text and image generation to music composition and code creation. The symbiotic relationship between data engineering and Gen AI is examined in detail, covering key aspects such as data collection and curation, preprocessing and transformation, scalable infrastruc
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Researcher. "THE CRITICAL ROLE OF DATA ENGINEERING IN GENERATIVE AI." International Journal of Computer Engineering and Technology (IJCET) 15, no. 4 (2024): 829–40. https://doi.org/10.5281/zenodo.13475740.

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This article explores the intricate relationship between data engineering and generative AI (Gen AI), highlighting the critical role that data engineering plays in the development, deployment, and optimization of Gen AI systems. It delves into the nature of generative AI and its revolutionary capabilities across various domains, from text and image generation to music composition and code creation. The symbiotic relationship between data engineering and Gen AI is examined in detail, covering key aspects such as data collection and curation, preprocessing and transformation, scalable infrastruc
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Researcher. "THE CRITICAL ROLE OF DATA ENGINEERING IN GENERATIVE AI." International Journal of Computer Engineering and Technology (IJCET) 15, no. 4 (2024): 829–40. https://doi.org/10.5281/zenodo.13475740.

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Abstract:
This article explores the intricate relationship between data engineering and generative AI (Gen AI), highlighting the critical role that data engineering plays in the development, deployment, and optimization of Gen AI systems. It delves into the nature of generative AI and its revolutionary capabilities across various domains, from text and image generation to music composition and code creation. The symbiotic relationship between data engineering and Gen AI is examined in detail, covering key aspects such as data collection and curation, preprocessing and transformation, scalable infrastruc
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Researcher. "THE CRITICAL ROLE OF DATA ENGINEERING IN GENERATIVE AI." International Journal of Computer Engineering and Technology (IJCET) 15, no. 4 (2024): 829–40. https://doi.org/10.5281/zenodo.13475740.

Full text
Abstract:
This article explores the intricate relationship between data engineering and generative AI (Gen AI), highlighting the critical role that data engineering plays in the development, deployment, and optimization of Gen AI systems. It delves into the nature of generative AI and its revolutionary capabilities across various domains, from text and image generation to music composition and code creation. The symbiotic relationship between data engineering and Gen AI is examined in detail, covering key aspects such as data collection and curation, preprocessing and transformation, scalable infrastruc
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Researcher. "THE CRITICAL ROLE OF DATA ENGINEERING IN GENERATIVE AI." International Journal of Computer Engineering and Technology (IJCET) 15, no. 4 (2024): 829–40. https://doi.org/10.5281/zenodo.13475740.

Full text
Abstract:
This article explores the intricate relationship between data engineering and generative AI (Gen AI), highlighting the critical role that data engineering plays in the development, deployment, and optimization of Gen AI systems. It delves into the nature of generative AI and its revolutionary capabilities across various domains, from text and image generation to music composition and code creation. The symbiotic relationship between data engineering and Gen AI is examined in detail, covering key aspects such as data collection and curation, preprocessing and transformation, scalable infrastruc
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Book chapters on the topic "Explainability generative AI"

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Barroso, Marta, Daniel Hinjos, Pablo A. Martin, Marta Gonzalez-Mallo, Victor Gimenez-Abalos, and Sergio Alvarez-Napagao. "Boosting AutoML and XAI in Manufacturing: AI Model Generation Framework." In Artificial Intelligence in Manufacturing. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46452-2_19.

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AbstractThe adoption of AI in manufacturing enables numerous benefits that can significantly impact productivity, efficiency, and decision-making processes. AI algorithms can optimize production schedules, inventory management, and supply chain operations by analyzing historical data and producing demand forecasts. In spite of these benefits, some challenges such as integration, lack of data infrastructure and expertise, and resistance to change need to be addressed for the industry to successfully adopt AI. To overcome these issues, we introduce the AI Model Generation framework (AMG), able t
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Stevens, Alexander, Johannes De Smedt, and Jari Peeperkorn. "Quantifying Explainability in Outcome-Oriented Predictive Process Monitoring." In Lecture Notes in Business Information Processing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_15.

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AbstractThe growing interest in applying machine and deep learning algorithms in an Outcome-Oriented Predictive Process Monitoring (OOPPM) context has recently fuelled a shift to use models from the explainable artificial intelligence (XAI) paradigm, a field of study focused on creating explainability techniques on top of AI models in order to legitimize the predictions made. Nonetheless, most classification models are evaluated primarily on a performance level, where XAI requires striking a balance between either simple models (e.g. linear regression) or models using complex inference structu
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Lavasa, Eleni, Christos Chadoulos, Athanasios Siouras, et al. "Toward Explainable Metrology 4.0: Utilizing Explainable AI to Predict the Pointwise Accuracy of Laser Scanning Devices in Industrial Manufacturing." In Artificial Intelligence in Manufacturing. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-46452-2_27.

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AbstractThe field of metrology, which focuses on the scientific study of measurement, is grappling with a significant challenge: predicting the measurement accuracy of sophisticated 3D scanning devices. These devices, though transformative for industries like manufacturing, construction, and archeology, often generate complex point cloud data that traditional machine learning models struggle to manage effectively. To address this problem, we proposed a PointNet-based model, designed inherently to navigate point cloud data complexities, thereby improving the accuracy of prediction for scanning
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Samek, Wojciech. "Explaining and Interpreting Generative AI." In The Oxford Handbook of the Foundations and Regulation of Generative AI. Oxford University Press, 2025. https://doi.org/10.1093/oxfordhb/9780198940272.013.0005.

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Abstract With the increasing success of deep AI models, understanding their decision-making processes and learning from the training data is pressing. Especially in sensitive applications (e.g. autonomous driving, medical diagnosis), the ability to explain is often considered essential for fostering trust, identifying biases, and ensuring the alignment of the model’s behaviour with human reasoning. Thus, the research field of explainable AI (XAI) has recently experienced significant growth. The motivations for explainability differ (e.g. AI developers may use explanation methods for model debu
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Panda, Rabi Shankar, Anjana Mishra, and Abhishek Mohanty. "Innovating Reality." In The Pioneering Applications of Generative AI. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-3278-8.ch004.

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Generative artificial intelligence has enormous promise in business, marketing, finance, education, and healthcare sectors. It can have an impact on areas like consumer engagement and fraud detection. But it also poses difficult problems. Decision-making is hampered by technological barriers like data quality, explainability, and authenticity, as well as economic issues like income inequality and possible job loss. Privacy, bias, and misuse are all examples of ethical dilemmas. To address these, thorough norms that guarantee accountability, openness, and equity are needed. Meeting societal req
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Patil, Dimple, Nitin Liladhar Rane, and Jayesh Rane. "Future directions for ChatGPT and generative artificial intelligence in various business sectors." In The Future Impact of ChatGPT on Several Business Sectors. Deep Science Publishing, 2024. http://dx.doi.org/10.70593/978-81-981367-8-7_7.

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ChatGPT and generative artificial intelligence have the potential to transform many business sectors. These technologies are changing customer engagement, operational efficiency, and strategic innovation. ChatGPT and generative AI models are streamlining processes, improving personalization, enabling predictive analytics, and supporting decision-making in healthcare, finance, retail, and education. Multimodal models will enable generative AI to integrate text, images, and other data types, expanding its use in complex scenarios like medical diagnostics, financial modeling, and virtual training
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Sindiramutty, Siva Raja, Krishna Raj V. Prabagaran, N. Z. Jhanjhi, Mustansar Ali Ghazanfar, Nazir Ahmed Malik, and Tariq Rahim Soomro. "Security Considerations in Generative AI for Web Applications." In Advances in Information Security, Privacy, and Ethics. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-5415-5.ch009.

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Protecting AI in web applications is necessary. This domain is a composite of technology and huge scope with good prospects and immense difficulties. This chapter covers the landscape of security issues with advancing generative AI techniques for integration into web development frameworks. The initial section is on security in web development—a conversation on the subtleties of generative AI-based methods. In a literal stance, the chapter offers 13 ways to approach it. Among the threats are those that introduce security issues related to generative AI deployments, which illustrate why it is v
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Kiradoo, Dr Giriraj. "Financial Forecasting with AI: Beyond Traditional Models." In Predictive Analytics in E-Commerce: A Quantitative Approach to Optimizing Customer Experience. San International Scientific Publications, 2023. http://dx.doi.org/10.59646/edbookc21/009.

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This chapter explores the symbiotic relationship between artificial intelligence (AI) and the financial services industry, offering an in-depth analysis of the transformative impact of AI on banking, investment, and risk management. The convergence of algorithms and intelligence is examined, with a specific focus on generative AI and its applications in creating financial narratives and visual content. The chapter covers key AI technologies, including machine learning, deep learning, natural language processing, computer vision, and robotic process automation, elucidating their roles in variou
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Sokolov, Artem. "ARTIFICIAL INTELLIGENCE FROM A TECHNICAL PERSPECTIVE." In DIGITALIZATION, METAVERSE, ARTIFICIAL INTELLIGENCE IN THE CONTEXT OF HUMAN AND INDIVIDUAL RIGHTS PROTECTION IN UKRAINE AND THE WORLD. SciFormat Publishing Inc., 2025. https://doi.org/10.69635/978-1-0690482-4-0-ch5.

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This monograph section provides a comprehensive analysis of the evolution, technological foundations, and current applications of artificial intelligence (AI), with a particular focus on its role in cybersecurity. We present a historical overview of AI development, tracing its path from the early conceptual ideas of the mid-20th century to the emergence of modern deep learning technologies, generative models, and large-scale Transformer architectures. Special attention is given to the critical technological breakthroughs that enabled the rapid growth of AI capabilities, including advances in c
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Tiwari, Daksh Chandra, Md Dilshad, and Samta Rani. "Ethical Implementation of Artificial Intelligence in the Healthcare Sector." In Next-Generation Computing: Trends and Challenges in Research. QTanalytics India, 2025. https://doi.org/10.48001/978-81-980647-3-8-2.

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AI has been utilized in medicine in various applications and has enhanced the efficiency and precision in healthcare. Its strengths are minimizing man handling, automation of processes in order to improve productivity, giving tailored recommendations, facilitating well-informed decision-making using actual time data analysis, predicting health outcomes, identifying risk, and ultimately improving patient care and satisfaction. But healthcare also raises ethical issues with the application of AI, including data security and privacy, discrimination bias. explainability, transparency, accountabili
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Conference papers on the topic "Explainability generative AI"

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Huang, Shiyuan. "Exploring Explainability and Interpretability in Generative AI." In Proceedings of the 20th Workshop of Young Researchers' Roundtable on Spoken Dialogue Systems. Association for Computational Linguistics, 2024. https://doi.org/10.18653/v1/2024.yrrsds-1.23.

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Vats, Shaurya, Sai Phani Chatti, Aravind Devanand, Sandeep Krishnan, and Rohit Karanth Kota. "Empowering LLMs for Mathematical Reasoning and Optimization: A Multi-Agent Symbolic Regression System." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.172269.

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Understanding data with complex patterns is a significant part of the journey toward accurate data prediction and interpretation. The relationships between input and output variables can unlock diverse advancement opportunities across various processes. However, most AI models attempting to uncover these patterns are not explainable or remain opaque, offering little interpretation. This paper explores an approach in explainable AI by introducing a multi-agent system (MaSR) for extracting equations between features using data. We developed a novel approach to perform symbolic regression by disc
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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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Sun, Jiao, Q. Vera Liao, Michael Muller, et al. "Investigating Explainability of Generative AI for Code through Scenario-based Design." In IUI '22: 27th International Conference on Intelligent User Interfaces. ACM, 2022. http://dx.doi.org/10.1145/3490099.3511119.

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Damen, Nicole B., Voho Seo, and Ye Wang. "Exploring Opportunities for Adopting Generative AI in Automotive Conceptual Design." In ASME 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/detc2024-143816.

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Abstract For AI to responsibly enable and enhance innovative design processes it is necessary to form an understanding of what processes and tools designers currently use, and why. This work employed remote interviews and an in-person workshop (respectively 8 and 6 different designers) to investigate the challenges and opportunities professional automotive designers anticipate towards generative AI tools in the conceptual design phase. The findings indicate that these designers prioritize novelty and value efficiency. Key challenges are finding relevant inspirational images and documentation d
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Tharayil, Sarafudheen M., Arwa Alnajashi, Abdullah Al-Ahamri, and Marwa Shahada. "A Framework for Predicting Pandemic Severity and Mortality Using Generative AI and LLMs." In SPE International Health, Safety, Environment and Sustainability Conference and Exhibition. SPE, 2024. http://dx.doi.org/10.2118/220429-ms.

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Abstract This research work introduces a comprehensive framework for pandemic severity and mortality prediction. The study achieves two key objectives. Firstly, a robust model is developed for accurate mortality prediction using deep learning mechanisms, leveraging variants like Convolutional Neural Networks, Recurrent Neural Networks, and Transformers which learn complex relationships from clinical and demographic data, enabling timely risk stratification. Secondly, enhancing model explainability through large language models (LLMs) for explainability. Integrating LLMs provides natural langua
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Mylrea, Michael, and Nikki Robinson. "AI Trust Framework and Maturity Model: A Zero Trust Approach to Zero Trust for AI Driven Autonomous Human Machine Teams & Systems." In 14th International Conference on Applied Human Factors and Ergonomics (AHFE 2023). AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1003760.

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The following article develops an AI Trust Framework and Maturity Model (AI-TFMM) to improve trust in AI technologies used by Autonomous Human Machine Teams & Systems (A-HMT-S). The framework establishes a methodology to improve quantification of trust in AI technologies. Key areas of exploration include security, privacy, explainability, transparency and other requirements for AI technologies to be ethical in their development and application. A maturity model framework approach to measuring trust is applied to improve gaps in quantifying trust and associated metrics of evaluation. Findin
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Weg, Joshua, Taehyung Wang, and Li Liu. "Interpretable AI-Generated Videos Detection using Deep Learning and Integrated Gradients." In 16th International Conference on Applied Human Factors and Ergonomics (AHFE 2025). AHFE International, 2025. https://doi.org/10.54941/ahfe1006041.

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The rapid advancements in generative AI have led to text-to-video models creating highly realistic content, raising serious concerns about misinformation spread through synthetic videos. As these AI videos become more convincing, they threaten information integrity across social media, news, and digital communications. Using AI-generated videos, bad actors can now create false narratives, manipulate public opinion, and influence critical processes like elections. This technology's democratization means that sophisticated disinformation campaigns are no longer limited to well-resourced actors,
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Seitzinger, Patrick, and Jay Kalra. "Artificial Intelligence in Healthcare: The Explainability Ethical Paradox." In 14th International Conference on Applied Human Factors and Ergonomics (AHFE 2023). AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1003466.

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Explainability is among the most debated and pivotal discussions in the advancement of Artificial Intelligence (AI) technologies across the globe. The development of AI in medicine has reached a tipping point in medicine with implications across all sectors. How we proceed with the issue of explainability will shape the direction and manner in which healthcare evolves. We require new tools that brings us beyond our current levels of medical understanding and capabilities. However, we limit ourselves to tools that we can fully understand and explain. Implementing a tool that cannot be fully und
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Khambampati, Subhash, Sushanth Dondapati, Tejo Vardhan Kattamuri, and Rahul Krishnan Pathinarupothi. "CureNet: Improving Explainability of AI Diagnosis Using Custom Large Language Models." In 2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON). IEEE, 2023. http://dx.doi.org/10.1109/smartgencon60755.2023.10442356.

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