Tag: explainable AI

This article introduces a unified and practical framework for explainable artificial intelligence (XAI), focusing on user-centric design principles to enhance trust and understanding in AI systems. It outlines key components and methodologies for developing AI that is not only powerful but also transparent and interpretable for end-users.

This analysis delves into the burgeoning field of explainable artificial intelligence (XAI) applied to DNA methylation patterns for brain tumor diagnostics. It explores how XAI is crucial for understanding the complex biological signals within methylation data, thereby enhancing diagnostic accuracy and trust in AI-driven medical tools.

This analysis delves into a novel explainable AI approach designed to interpret deep neural networks (DNNs) that have been regionally optimized for hydrological prediction. The research highlights the critical need for transparency in AI models used for water resource management, offering a pathway to understand the 'why' behind complex predictions.

This deep dive evaluates the application of Explainable AI (XAI) in analyzing deep learning models for rice leaf disease detection, focusing on qualitative and quantitative assessments to enhance model transparency and reliability in agricultural applications.

This deep-dive explores a novel approach to predicting organic photovoltaic (OPV) performance, leveraging Bayesian optimization and explainable AI. The method promises enhanced accuracy and interpretability, crucial for accelerating OPV technology development.

Researchers have employed explainable artificial intelligence (XAI) to unlock the complex 'language' of sticky proteins, a breakthrough that could revolutionize drug discovery and materials science by enabling precise control over protein interactions.

A University of Nebraska–Lincoln project led by Assistant Professor of Computer Science and Engineering, Dr. Debashis Choudhury, is pioneering the use of explainable artificial intelligence (XAI) to enhance transparency and trust in agricultural decision-making. This initiative aims to make AI models understandable to farmers, fostering wider adoption and more informed practices.