Lucyde is introduced, a web-based demonstrator designed to help users explore, compare, and better understand XAI methods across different datasets, models, and configurations, and enables side-by-side comparison of methods and offers easy-to-understand supplementary information for different user groups.
Explainable Artificial Intelligence (XAI) has become a key research focus nowadays due to the growing use of more intricate machine learning and deep learning systems in high-stakes systems. Although contemporary artificial intelligence (AI) methods show impressive prediction accuracy, the lack of transparency, a characteristic of their opaque (black-box) essence, presents serious forestalling issues in the areas of transparency, trust, accountability, and regulatory compliance. This interpretability is a disadvantage as numerous areas, like healthcare, finance, autonomous systems, and governance of the people, need AI systems to be applied in areas that are sensitive and require decision-making in a way that is comprehensible and explainable to human participants. XAI aims to solve these dilemmas by creating approaches and systems that allow human operators to comprehend, trust, and be able to handle AI-motivated decisions. XAI is not only aimed at providing explanations, but also at making these explanations meaningful, faithful to underlying model and applicable by various groups of users such as domain experts, developers, and policymakers. Enabling transparency, XAI leads to ethical AI, reduces bias and enhances debugging and model checking, and enables compliance with the developing regulatory frameworks like the General Data Protection Regulation (GDPR). This paper constitutes a thorough discussion of the XAI, as applied on transparent decision systems. It starts with a general introduction to motivation and the conceptualization of explainability in AI and goes on to provide a comprehensive literature review of model-specific and model-agnostic explainability algorithms. The suggested methodology combines both local and global explanatory approaches and transparency leadership framework. Experimental findings show the effectiveness of XAI techniques to enhance interpretability without causing a major loss in predictive accuracy. Lastly, the paper provides the practical implications, limitations, and research directions on the future of explainable and trustworthy AI systems.
Unknown authors· International Journal of App...· 0 citations
XAI provides a powerful framework for responsible AI development, challenges such as the performance-interpretability trade-off, lack of standardized evaluation metrics, and potential for human misinterpretation remain areas of active research.
P. Pradhan, Amol Rajmane, C. patil· Journal of image processing...· 0 citations
It is concluded that explainability is a necessary, though not sufficient, condition for trustworthy Al, and concrete directions for future research are outlined, including standardised benchmarks, human-centred evaluation, and explainability for large generative models.
Afna Ashraff M, Archana K, Buthaina Buthaina et al.· International Journal of Tec...· 0 citations
The study suggests that explainable AI is a crucial factor in building intelligent, reliable, accountable, transparent, and effective AI-powered systems that can be deployed in realistic environments for decision making.
Kirankumar Pundlik Mohurle, S. Sahare, Yugant Rupesh Dhoke et al.· International Journal of Eng...· 0 citations
A methodology is advanced to embed explainability in the AI decision-making process, starting from data preprocessing to generating explanations and human evaluation, and the results highlight the potential of explainability to enhance human comprehension and foster responsible use of AI systems in high-stakes decision-making scenarios.
Mahabala H. N.· International Journal of Mod...· 0 citations
Most artificial intelligence (AI) models used in radiology are black boxes-they produce predictions without explaining the basis of their outputs, raising concerns about clinical safety, accountability, and trust. To address this, a growing body of methods has been developed to help clinicians understand and evaluate AI predictions. This field, known as explainable AI (XAI), aims to help clinicians interrogate, interpret, and critically evaluate AI predictions by identifying factors associated with model outputs. In this educational and practical review, we provide an accessible overview of XAI tailored for practicing radiologists and physicians. We cover the major categories of explanation methods, including saliency maps, perturbation-based and feature-attribution approaches, concept- based methods, and example-based reasoning, as well as uncertainty quantification as a complementary approach for assessing prediction reliability, along with common misconceptions and emerging regulatory obligations. We aim to make XAI easier for healthcare professionals to understand, as effective oversight of AI tools has become a core competency for the modern radiologist.
Gorkem Durak, H. Aktas, Tugba Akinci D’Antonoli et al.· Diagnostic and Interventiona...· 0 citations
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