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Responsible AI with Azure

Develop, use, and govern AI solutions responsibly with Azure AI.

Build responsible AI solutions with Azure Machine Learning

Apply responsible AI throughout the machine learning development lifecycle to build fair, explainable, and performant applications that earn the trust of customers. The responsible AI dashboard consolidates responsible AI capabilities to support deep-dive investigations in your flow of work, while model monitoring helps you optimize performance in production. Contextualize responsible AI metrics for business audiences with the responsible AI scorecard (preview) to streamline AI governance, compliance, and collaboration.

Develop responsibly for fairness and explainability

Quickly assess your machine learning model with state-of-the-art algorithms in the responsible AI dashboard, including tabular, image, and text models. Using reproducible and automated workflows, perform statistical modeling, exploratory data analysis, error analysis, counterfactual analysis, and causal analysis, and evaluate your model for fairness, interpretability, and performance, all within an end-to-end machine learning platform.

Reinforce responsible AI in production

Use counterfactuals to understand what’s needed to produce a specific outcome and causal analysis to proactively apply new policies and effect real-world change. Then, track and optimize model performance in production with model monitoring. Simplify inference data collection and get timely alerts on data drift, feature attribute drift, and data quality issues to continually improve model performance and business outcomes.

Govern for transparency and accountability

Protect and govern your machine learning assets with enterprise privacy and security controls, 60+ compliance certifications, and machine learning operations (MLOps). Easily track and understand data lineage and use the connector for Microsoft Purview to streamline metadata storage and perform faster root cause analyses. Export responsible AI scorecards (preview) for your machine learning models to contextualize responsible AI metrics for business stakeholders and foster proactive collaboration.

Build responsibly for trusted outcomes

Operationalize responsible AI to deliver trusted outcomes. Assess models for fairness, reliability, and explainability

Make real-time, data-driven decisions with confidence. Monitor and optimize AI model performance in production

Protect and govern your machine learning assets for transparency, accountability, and compliance across stakeholder groups

Related products

Azure Machine Learning

Use an enterprise-grade service for the end-to-end machine learning lifecycle.

Azure AI Services

Add cognitive capabilities to apps with APIs and AI services.

cover of Driving Business Value with Responsible AI book

Driving Business Value with Responsible AI webinar

Watch on demand: Driving Business Value with Responsible AI.

Build your machine learning skills with Azure

Learn more about machine learning on Azure and participate in hands-on tutorials with a 30-day learning journey. By the end, you'll be prepared to take the Azure Data Scientist Associate Certification.

Customers are putting responsible AI into practice

"With Azure Machine Learning and the Responsible AI dashboard, we have the tools we need to understand, refine, and explain our outcomes so we can better serve our patients."

Dr. Justin Green, Leadership and Management Fellow at Health Education England North & Orthopedic Surgical Registrar

A doctor sitting with a patient and reviewing their health data on a tablet
"With model interpretability in Azure Machine Learning, we have a high degree of confidence that our machine learning model is generating meaningful and fair results."

Daniel Engberg, Head of Data Analytics and AI, Scandinavian Airlines (SAS)

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