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In today’s rapidly evolving ML landscape, ensuring robust, fair, and ethical model usage is paramount. Achieving these objectives requires rigorous evaluation of performance, bias, variance, inference time, ethical considerations, and regulatory compliance, yet existing approaches often lack accessibility for non-technical stakeholders. Additionally, many models function as black boxes, complicating assessment when training data is unavailable-especially in medical contexts where privacy is a concern. ML-EvalPro addresses these challenges through a user-friendly platform that thoroughly evaluates black-box models without requiring direct access to training data. By demystifying complex metrics, it enables informed decision-making and promotes transparency, accountability, and ethical alignment. Ultimately, ML-EvalPro aims to foster safer, fairer, and more effective integration of machine learning in high-stakes domains such as healthcare.

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ML-EvalPro: Machine Learning Evaluation Profiler for Supervised Tasks

  • 会议论文

  • 发表日期：

    2025年

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  • Mohamed Maher,
  • Reem Ayman,
  • Sondos Akram,
  • Omar Marie,
  • Radwa ElShawi

摘要

In today’s rapidly evolving ML landscape, ensuring robust, fair, and ethical model usage is paramount. Achieving these objectives requires rigorous evaluation of performance, bias, variance, inference time, ethical considerations, and regulatory compliance, yet existing approaches often lack accessibility for non-technical stakeholders. Additionally, many models function as black boxes, complicating assessment when training data is unavailable-especially in medical contexts where privacy is a concern. ML-EvalPro addresses these challenges through a user-friendly platform that thoroughly evaluates black-box models without requiring direct access to training data. By demystifying complex metrics, it enables informed decision-making and promotes transparency, accountability, and ethical alignment. Ultimately, ML-EvalPro aims to foster safer, fairer, and more effective integration of machine learning in high-stakes domains such as healthcare.