Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 47 for “"XAI"”.
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C-XAI: Design Method for Explainable AI Interfaces to Enhance Trust Calibration.
… and satisfaction. Integrating eXplainable AI (XAI) into AI-based decision-making tools has become a crucial requirement for a safe and effective human-AI collaborative environment. Recently, the impact of explainability on trust calibration has become a main research question. The role refers …
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Multi-scale local explanation approach for image analysis using model-agnostic explainable artificial intelligence (XAI)
The recent success of deep neural networks has generated a remarkable growth in Artificial Intelligence (AI) research, and it received much interest over the past few years. However, one of the main challenges for the broad adoption of deep learning based models such as Convolutional Neural …
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A Feasible Situation Awareness-Based Evaluation Framework for Quality of Machine Learning Explanations
EXplainable Artificial Intelligence (XAI) has emerged as a critical domain, with the aim of enhancing the transparency and interpretability of advanced machine learning (ML) models. As the need to introduce more complicated ML in broader industries surged, especially for industries with high …
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Explainable AI Methods For Enhancing AI-Based Network Intrusion Detection Systems
… this thesis proposes end-to-end explainable AI (XAI) frameworks tailored to enhance the understandability and performance of AI models in this context.The first chapter benchmarks seven black-box AI models across one real-world and two benchmark network intrusion datasets, laying the foundation …
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Advancing Pattern Detection, Theory Development and Decision Making with Explainable AI
… of explainable artificial intelligence (XAI) methods in data-driven decision-making and computationally intensive theory development (CTD) is a subject of ongoing debate, particularly concerning how and whether these methods can be effectively employed, and how the reliability of their …
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Explainability of non-deterministic solvers: explanatory feature generation from the data mining of the search trajectories of population-based metaheuristics.
… intelligence (AI), the field of explainable AI (XAI) has grown significantly as machine learning, systems that mimic human reasoning and other AI systems have continued to be adopted into more and more user-critical applications. XAI as a research area aims, among many things, to aid in gaining a …
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Advancing Explainability in Multi-Label Classification for Tomato Disease Detection Using Machine Learning Interpretability Techniques
… techniques, known as Explainable AI (XAI) has emerged as a solution, providing insights into how these models make predictions and which features of the input data most influence their decisions. This research aims to combine CNNs with XAI techniques to enhance transparency in plant …
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Explainable AI: A Unified Approach Based on Cooperative Game Theory
Explainable Artificial Intelligence (XAI) aims to enhance the interpretability of machine learning (ML) models, yet existing feature-based explanation methods remain fragmented across local and global approaches. This thesis presents a unified framework based on cooperative game theory to …
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Explainable Deep Learning Approach for Detecting Money Laundering Transactions in Banking System
… using SHapley Additive exPlanations (SHAP) XAI method. The results showed that the CNN model outperformed other models, indicating better handling of compliance risk. On the contrary, CNN model showed higher number of false positives compared to other models which indicates more operational …
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Transparent Value Alignment: Foundations for Human-Centered Explainable AI in Alignment
… process through approaches for explainable AI (XAI) can enable humans to more efficiently and effectively teach robots about their goals. In this thesis, we introduce the Transparent Value Alignment (TVA) paradigm which captures this two-way communication and inference process and discuss …
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The Role of Explainable Artificial Intelligence in Data Science [Il Ruolo dell'Intelligenza Artificiale Esplicabile nella Scienza dei Dati]
… dell'Intelligenza Artificiale Esplicabile (XAI), che include metodi specifici per modello e indipendenti dal modello. Questo comporta un'analisi dettagliata di tecniche come l'analisi dell'importanza delle caratteristiche, Local Interpretable Model-agnostic Explanations (LIME) e SHapley …
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Enhancing Interpretability: The Role of Concept-based Explanations Across Data Types
… is addressed by the field of Explainable AI (XAI). Recently, Concept-based explanations (CbEs) have emerged as a powerful new XAI paradigm, providing model explanations in terms of human-understandable units, rather than individual features, pixels, or characters. Despite their numerous …
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Explainable Neural Claim Verification Using Rationalization
… the system using statistical and Explainable AI (XAI) metrics to ensure the outcomes are valid, verified, and trustworthy to help reinforce the human-AI trust. We propose a new subfield in XAI called Rational AI (RAI) to improve research progress on rationalization and NLE-based explainability …
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Prediction of Gravel Streambed Embeddedness Using Explainable AI and Machine Learning Techniques
… data and explainable artificial intelligence (XAI) to uncover important variables related to physical processes affecting embeddedness in Virginia (VA) and the United States (U.S.). The VA model used 1,125 embeddedness measurements from 906 sites provided by the VA Department of Environmental …
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Effects of Logic-Style Explanations and Uncertainty on Users’ Decisions
… The eXplainable Artificial Intelligence (XAI) community investigated numerous factors influencing subjective and objective metrics in the user-AI team, such as the effects of presenting AI-related information and explanations to users. Nevertheless, some factors that influence the …
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From local explanations to comprehensive mechanistic understanding of deep vision models
… field of eXplainable Artificial Intelligence (XAI) has introduced techniques like attribution maps and feature visualizations to illuminate singular aspects of model behavior. Yet, achieving a comprehensive understanding that enables validation and control of the complex mechanisms inside AI …
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IoT network Malicious Behaviour Profiling Based on Explainable AI Using LSTM and SHAP
… and identification profiling model using XAI. The proposed model introduces a novel feature selection techqnique with the XGBoost algorithm and a correlation-based feature selection technique to enhance efficiency. An optimized LSTM neural network enables accurate bot detection and …
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Explainable AI in Medical Imaging: An Interdisciplinary Translational Approach
… increasingly challenging, making explainable AI (XAI) a crucial component of modern AI systems. The focus of this work is to integrate these new technologies alongside foundational methods of image processing to create tools that can be used by domain experts who are not programmers. Prior to …
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A Sociotechnical Perspective on Digital Transformation of Work in Organizations, on Platforms, and in Academia
… work, we demonstrated how ML combined with XAI applications can be employed in theory development.
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NETWORKS OF GROUP EQUIVARIANT NON-EXPANSIVE OPERATORS FOR ARTIFICIAL INTELLIGENCE. MODELS, APPLICATIONS AND INTERPRETABILITY.
… pursuit of eXplainable Artificial Intelligence (XAI) aims to develop methods that clarify the decision-making processes of black-box AI systems, making them more understandable and trustworthy for end users, in line with regulatory and policy demands. Another significant challenge facing XAI is …
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