For more information on how to implement Responsible AI in Azure Machine Learning, see Responsible AI dashboard. Learn how to generate the Responsible AI dashboard via CLI and SDK or Azure Machine Learning studio UI. Learn how to generate a Responsible AI scorecard based on the insights observed...
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Microsoft developed aResponsible AI Standard. It's a framework for building AI systems according to six principles: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. For Microsoft, these principles are the cornerstone of a responsible and trustworthy...
负责任的 AI 是一个旨在完全做到这一点的治理框架。 负责任的 AI 可以包含有关可收集和使用哪些数据的详细信息,以及如何实现、评估和监视模型。 还可以定义谁负责 AI 实现的负面结果。 可以定义特定方法和其他更开放的解释方法。 他们都寻求实现相同的目的:创建可解释、公平、安全和尊重用户隐私的 AI 系统。 本...
Learn more about responsible AI Microsoft responsible AI resources Microsoft Azure Learning course on responsible AI Additional resources Training Module Introduction to Microsoft's Responsible AI Approach - Training This module provides an overview of Microsoft's responsible AI principles, guidelines, and ...
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Using the responsible AI dashboard, evaluate with reproducible and automated workflows to assess model fairness, causal analysis, exploratory data analysis, and more.
content into the user’s specified language within Teams using Azure Cognitive Services. To check if Instructions generation supports a given language, consult the ACS Translator’s supported languages list:Language support - Translator - Azure AI services | Microsoft Learn....
Using the responsible AI dashboard, evaluate with reproducible and automated workflows to assess model fairness, causal analysis, exploratory data analysis, and more.
Robust machine learning for responsible AI. Contribute to microsoft/robustlearn development by creating an account on GitHub.