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.
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
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.
Researchers explore automation in digital forensics and the challenges, considerations, and perspectives surrounding automation adoption.
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