Explainable AI News and Research

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Explainable AI (XAI) refers to methods and techniques in the application of artificial intelligence such that the results of the solution can be understood by human experts. It contrasts with the concept of the "black box" in machine learning where even their designers cannot explain why the AI arrived at a specific decision. XAI is crucial for building trust in AI systems and for their ethical and fair use.

Transforming Lithium-Ion Battery Tech with Explainable AI

This comprehensive review explores the applications of Explainable Machine Learning (XML) in the realm of lithium-ion batteries. It sheds light on how XML techniques enhance transparency, inform decision-making, and drive innovation across battery production, state estimation, and management systems.

6 Sep 2023

survex: Transparent AI for Survival Analysis in Healthcare

Researchers introduce "Survex," an R package designed to enhance transparency and accountability in machine learning survival models, particularly in healthcare applications. Survex offers tailored explanations for survival models, addressing concerns over model reliability and fairness, and promotes responsible AI adoption in sensitive areas by providing insights into the rationale behind predictions.

3 Sep 2023

Qualitative eXplainable Graphs: Unveiling Interpretability in Automated Driving

Researchers explore the innovative concept of Qualitative eXplainable Graphs (QXGs) for spatiotemporal reasoning in automated driving scenes. Learn how QXGs efficiently capture complex relationships, enhance transparency, and contribute to the trustworthy development of autonomous vehicles. This groundbreaking approach revolutionizes automated driving interpretation and sets a new standard for dependable AI systems.

29 Aug 2023

Harnessing Soft Computing for Cardiovascular Disease Prediction and Diagnosis

This review explores how fuzzy logic, neural networks, and optimization algorithms hold immense promise in predicting, diagnosing, and detecting CVD. By handling complex medical uncertainties and delivering accurate and affordable insights, soft computing has the potential to transform cardiovascular care, especially in resource-limited settings, and significantly improve clinical outcomes.

27 Aug 2023

Boosting Energy Efficiency in 6G Networks with Intelligent Reflecting Surfaces

Researchers delve into the transformative potential of Intelligent Reflecting Surfaces (IRSs) within 6G wireless networks, presenting a model that enhances communication efficiency and energy conservation. A case study involving smart ocean transportation illustrates the advantages of using IRSs, offering real-world insights into their practical implementation for Industry 5.0 applications.

10 Aug 2023

Federated Learning as a Service: Enhancing Trustworthy AI in B5G/6G Networks

https://www.sciencedirect.com/science/article/pii/S0140366423002724?via%3Dihub

2 Aug 2023

Demystifying AI: A Comprehensive Overview of eXplainable AI (XAI) for Trustworthy Decision-Making

Demystifying AI: A comprehensive overview of eXplainable AI (XAI) provides a thorough analysis of current trends, research, and concerns in the field, shedding light on the inner workings of AI models for trustworthy decision-making. The review covers various aspects of XAI, including data explainability, model explainability, post-hoc explainability, assessment of explanations, and available XAI research software tools. It highlights the importance of understanding and validating AI systems to ensure transparency, fairness, and accountability in their deployment

9 Jul 2023

Unleashing the Potential of Data: Paving the Way for 6G Systems

Data-driven insights and analytics are shaping the evolution towards 6G systems, as the growth of data traffic and convergence of technologies become crucial. A case study on Fed-XAI demonstrates the potential of leveraging data for AI operations and quality of service predictions, showcasing the practical applications of data-driven innovation in developing 6G networks.

4 Jul 2023