{"id":1326,"date":"2025-09-29T07:54:06","date_gmt":"2025-09-29T07:54:06","guid":{"rendered":"https:\/\/scipapermill.com\/index.php\/2025\/09\/29\/explainable-ai-illuminating-the-black-box-across-domains-2\/"},"modified":"2025-12-28T22:05:40","modified_gmt":"2025-12-28T22:05:40","slug":"explainable-ai-illuminating-the-black-box-across-domains-2","status":"publish","type":"post","link":"https:\/\/scipapermill.com\/index.php\/2025\/09\/29\/explainable-ai-illuminating-the-black-box-across-domains-2\/","title":{"rendered":"Explainable AI: Illuminating the Black Box Across Domains"},"content":{"rendered":"

Latest 50 papers on explainable ai: Sep. 29, 2025<\/h3>\n

Explainable AI: Illuminating the Black Box Across Domains<\/h2>\n

In the rapidly evolving landscape of AI and Machine Learning, the demand for transparency and trustworthiness has become paramount. As AI systems become more powerful and ubiquitous, particularly in high-stakes fields like healthcare, cybersecurity, and autonomous systems, understanding why<\/em> a model makes a certain decision is no longer a luxury but a necessity. This drive for clarity forms the core of Explainable AI (XAI), a field dedicated to making complex AI models more interpretable and accessible. Recent research, synthesized from a collection of innovative papers, showcases significant strides in moving XAI from a theoretical concept to practical, impactful applications across diverse domains.<\/p>\n

The Big Idea(s) & Core Innovations<\/h2>\n

At its heart, recent XAI research grapples with two central challenges: achieving high performance without sacrificing interpretability and ensuring that explanations are truly useful and understandable to human users. A common thread throughout these papers is the integration of XAI into<\/em> the model design or decision-making process, rather than as a post-hoc add-on. For instance, the groundbreaking work by Xiuqi Ge et al.<\/strong> from the University of Electronic Science and Technology of China, in their paper \u201cMedical Priority Fusion: Achieving Dual Optimization of Sensitivity and Interpretability in NIPT Anomaly Detection<\/a>\u201d, introduces Medical Priority Fusion (MPF), a framework that simultaneously optimizes diagnostic accuracy and interpretability in non-invasive prenatal testing (NIPT). This represents a crucial step towards clinically deployable solutions that balance performance with transparency, especially in critical medical applications.<\/p>\n

Another significant innovation comes from Patrick Wienholt et al.<\/strong>, whose \u201cMedicalPatchNet: A Patch-Based Self-Explainable AI Architecture for Chest X-ray Classification<\/a>\u201d introduces an inherently self-explainable deep learning architecture. Instead of relying on post-hoc methods, MedicalPatchNet provides transparent attribution of decisions to specific image regions, effectively mitigating risks like \u2018shortcut learning\u2019 and enhancing clinical trust. Similarly, Ahad, M. T. et al.<\/strong> in \u201cR-Net: A Reliable and Resource-Efficient CNN for Colorectal Cancer Detection with XAI Integration<\/a>\u201d and Saifuddin Sagor et al.<\/strong> with \u201cA study on Deep Convolutional Neural Networks, transfer learning, and Mnet model for Cervical Cancer Detection<\/a>\u201d both integrate XAI techniques like LIME, SHAP, and Grad-CAM directly into their medical imaging models to provide high accuracy with clear diagnostic insights, moving beyond black-box predictions.<\/p>\n

The push for interpretability extends beyond healthcare. Tiouti Mohammed et al.<\/strong>, from Universit\u00e9 d\u2019Evry-Val-d\u2019Essonne and Audensiel Conseil, present \u201cMetaLLMix : An XAI Aided LLM-Meta-learning Based Approach for Hyper-parameters Optimization<\/a>\u201d, a zero-shot hyperparameter optimization framework. This innovative approach uses meta-learning, XAI (specifically SHAP), and efficient LLM reasoning to significantly reduce optimization time while providing transparent, natural language explanations for chosen configurations. This not only speeds up development but also offers clear rationales, a major leap for AutoML.<\/p>\n

Moreover, the concept of XAI is being refined to address specific human needs and contexts. \u201cSee What I Mean? CUE: A Cognitive Model of Understanding Explanations<\/a>\u201d by Tobias Labarta et al.<\/strong> from Fraunhofer Heinrich Hertz Institute, delves into the cognitive aspects of understanding explanations, particularly for visually impaired users. Their CUE model validates how explanation properties like legibility impact user comprehension, emphasizing that \u2018accessibility by design\u2019 doesn\u2019t always translate to \u2018accessibility in practice.\u2019 This highlights a crucial area for user-centered XAI design.<\/p>\n

In the realm of security, Md. Alamgir Hossain et al.<\/strong>\u2019s \u201cA Comparative Analysis of Ensemble-Based Machine Learning Approaches with Explainable AI for Multi-Class Intrusion Detection in Drone Networks<\/a>\u201d leverages XAI for robust intrusion detection in drone networks, using SHAP and LIME to enhance transparency and trust in critical operations. Similarly, Author A et al.<\/strong> in \u201crCamInspector: Building Reliability and Trust on IoT (Spy) Camera Detection using XAI<\/a>\u201d achieve over 99% accuracy in IoT camera detection while ensuring interpretability through SHAP and LIME, proving XAI\u2019s value in practical cybersecurity.<\/p>\n

Under the Hood: Models, Datasets, & Benchmarks<\/h2>\n

These advancements are often powered by novel architectures, curated datasets, and rigorous benchmarking, enabling both performance and interpretability:<\/p>\n