仪式:一个量化监督机器学习可信度的平台

Alberto Huertas Celdrán, Jan Bauer, Melike Demirci, Joel Leupp, M. Franco, Pedro Miguel Sánchez Sánchez, Gérôme Bovet, G. Pérez, B. Stiller
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引用次数: 1

摘要

这个演示展示了RITUAL,一个由新算法和Web应用程序组成的平台,根据其公平性、可解释性、鲁棒性和可问责性来量化监督机器和深度学习(ML/DL)模型的可信度水平。该算法部署在Web应用程序上,允许用户量化和比较其ML/DL模型的可信度。最后,使用ML/DL模型对网络攻击进行分类的场景验证了该平台的适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
RITUAL: a Platform Quantifying the Trustworthiness of Supervised Machine Learning
This demo presents RITUAL, a platform composed of a novel algorithm and a Web application quantifying the trustworthiness level of supervised Machine and Deep Learning (ML/DL) models according to their fairness, explainability, robustness, and accountability. The algorithm is deployed on a Web application to allow users to quantify and compare the trustworthiness of their ML/DL models. Finally, a scenario with ML/DL models classifying network cyberattacks demonstrates the platform applicability.
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