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AI Trustworthiness and Risk Assessment for Challenged Contexts (ATRACC) - Call For Papers

  • 1.  AI Trustworthiness and Risk Assessment for Challenged Contexts (ATRACC) - Call For Papers

    Posted 07-11-2024 15:03
    Edited by Achraf Cohen 07-11-2024 15:03

    AI researchers and engineers are confronted with different levels of safety and security, different horizontal and vertical regulations, different (ethical) standards (including fairness, privacy), different certification processes, and different degrees of liability, that force them to examine a multitude of trade-offs and alternative solutions to address specific requirements, such as fairness, explainability, transparency, accountability, reproducibility, reliability, and acceptance.  It is critical to establish objective attributes such as accountability, accuracy, controllability, correctness, data quality, reliability, resilience, robustness, safety, security, transparency, explainability, fairness, privacy etc, map them onto the AI processes and its lifecycle and provide metrics, measurement, methods and tools to assess them. This emphasis needs also to be considered at the theoretical level, so that AI process and lifecycle considerations, which are often only addressed after the research methods are developed and incorporated early, bringing full cycle concerns closer to AI basic research approaches.

    The focus of this symposium track is on AI trustworthiness broadly and methods that help provide bounds for fairness, reproducibility, reliability and accountability in the context of quantifying AI-system risk, spanning the entire AI lifecycle from theoretical research formulations all the way to system implementation, deployment and operation.  This symposium brings together industry, academia, and government researchers and practitioners who are vested stakeholders in addressing these AI-specific and intelligent system challenges in applications where a priori understanding of risk is critical.

    The symposium track aims to create a platform for discussions and explorations that are expected to ultimately contribute to the development of innovative solutions for quantitatively trustworthy AI. Potential topics of interest include, but are not limited to:

    Best,



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    Achraf Cohen, Ph.D.
    Associate Professor of Statistics and Data Science
    Department of Mathematics and Statistics
    University of West Florida

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