Test Your Understanding of Responsible AI in Azure Machine Learning

Understanding Responsible AI and Its Implementation in Azure Machine Learning

• Responsible AI is an approach to developing, assessing, and deploying AI systems in a safe, trustworthy, and ethical way.
• Microsoft has developed a Responsible AI Standard consisting of six principles: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability.
• Azure Machine Learning supports tools for enabling developers and data scientists to implement and operationalize the six principles.
• The fairness assessment component of the Responsible AI dashboard enables data scientists and developers to assess model fairness across sensitive groups defined in terms of gender, ethnicity, age, and other characteristics.
• The error analysis component of the Responsible AI dashboard enables data scientists and developers to identify discrepancies in the model's performance across different demographic groups or infrequently observed input conditions in the training data.
• The model interpretability and counterfactual what-if components of the Responsible AI dashboard enable data scientists and developers to generate human-understandable descriptions of the predictions of a model and understand how it reacts to feature changes and perturbations.
• Azure Machine Learning also supports a Responsible AI scorecard, a customizable PDF report that developers can use to educate their stakeholders about their datasets and models' health, compliance, and trustworthiness.
• Azure Machine Learning enables administrators and developers to create a secure configuration that complies with their companies' policies, including privacy laws that require access to data for AI systems to make accurate predictions and decisions about people.
• Microsoft has also created two open-source packages, Differential Privacy and Confidential Computing, that can enable further implementation of privacy and security principles.
• Machine learning operations (MLOps) principles and practices increase the efficiency of AI workflows and create accountability for how AI systems operate.
• Azure Machine Learning provides various MLOps capabilities, such as model versioning, automated deployment, and monitoring, to ensure the reliability and safety of AI systems.
• The Responsible AI scorecard in Azure Machine Learning creates accountability by enabling cross-stakeholder communications and empowering developers to share their model health insights with their technical and non-technical stakeholders about AI data and model health.

Understanding Responsible AI and Its Implementation in Azure Machine Learning

• Responsible AI is an approach to developing, assessing, and deploying AI systems in a safe, trustworthy, and ethical way.
• Microsoft has developed a Responsible AI Standard consisting of six principles: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability.
• Azure Machine Learning supports tools for enabling developers and data scientists to implement and operationalize the six principles.
• The fairness assessment component of the Responsible AI dashboard enables data scientists and developers to assess model fairness across sensitive groups defined in terms of gender, ethnicity, age, and other characteristics.
• The error analysis component of the Responsible AI dashboard enables data scientists and developers to identify discrepancies in the model's performance across different demographic groups or infrequently observed input conditions in the training data.
• The model interpretability and counterfactual what-if components of the Responsible AI dashboard enable data scientists and developers to generate human-understandable descriptions of the predictions of a model and understand how it reacts to feature changes and perturbations.
• Azure Machine Learning also supports a Responsible AI scorecard, a customizable PDF report that developers can use to educate their stakeholders about their datasets and models' health, compliance, and trustworthiness.
• Azure Machine Learning enables administrators and developers to create a secure configuration that complies with their companies' policies, including privacy laws that require access to data for AI systems to make accurate predictions and decisions about people.
• Microsoft has also created two open-source packages, Differential Privacy and Confidential Computing, that can enable further implementation of privacy and security principles.
• Machine learning operations (MLOps) principles and practices increase the efficiency of AI workflows and create accountability for how AI systems operate.
• Azure Machine Learning provides various MLOps capabilities, such as model versioning, automated deployment, and monitoring, to ensure the reliability and safety of AI systems.
• The Responsible AI scorecard in Azure Machine Learning creates accountability by enabling cross-stakeholder communications and empowering developers to share their model health insights with their technical and non-technical stakeholders about AI data and model health.