Responsible machine learning (responsible ML)

Azure Machine Learning capabilities that empower data scientists and developers to innovate responsibly.

Understand, protect, and control your data, models, and processes to build trusted solutions.

State-of-the-art technology enabling responsible ML development, deployment, and use. Put responsible AI principles into practice and build trust throughout the ML lifecycle.

Understand

Gain visibility into your models, explain model behavior, and detect and mitigate unfairness—all with out-of-the-box visualizations.

Protect

Apply differential privacy techniques to protect sensitive data and prevent leaks. Encrypt data and build models in a secure environment to maintain confidentiality.

Control

Use built-in lineage and audit trial capabilities and enable responsible process by documenting model metadata to meet regulatory requirements.

Understand your models and build for fairness

Explain model behavior and uncover features that have the most impact on predictions. Use built-in explainers for both glass-box and black-box models during model training and inferencing. Use interactive visualizations to compare models and perform what-if analysis to improve model accuracy. Test your models for fairness using state-of-the-art algorithms. Mitigate unfairness throughout the ML lifecycle, compare mitigated models, and make intentional fairness versus accuracy trade-offs as desired.

Protect data privacy and confidentiality

Build models that preserve privacy using the latest innovations in differential privacy, which injects precise levels of statistical noise in data to limit the disclosure of sensitive information. Identify data leaks and intelligently limit repeat queries to manage exposure risk.

Use encryption and confidential machine learning (coming soon) techniques specifically designed for machine learning to securely build models using confidential data.

Control and govern through every step of the ML process

Access built-in capabilities to automatically track lineage and create an audit trial across the ML lifecycle. Obtain full visibility into the ML process by tracking datasets, models, experiments, code, and more. Use custom tags to implement model datasheets, document key model metadata, increase accountability, and ensure responsible process.

See responsible ML in action

Get better visibility into models to explain their behavior

Get better visibility into models to explain their behavior

Make critical decisions with models that are without hidden biases

Make critical decisions with models that are without hidden biases

Using differential privacy techniques to protect sensitive datasets

Using differential privacy techniques to protect sensitive datasets

Customers using responsible ML

"Azure Machine Learning and its Fairlean capabilities offer advanced fairness and explainability that have helped us deploy trustworthy AI solutions for our customers, while enabling stakeholder confidence and regulatory compliance."

Alex Mohelsky, Partner and Data Analytics Leader at EY Canada

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Ernst & Young

"Azure Machine Learning helps us build AI responsibly and build trust with our customers. Using the interpretability capabilities in the fraud detection efforts for our loyalty program, we are able to understand models better, identify genuine cases of fraud, and reduce the possibility of erroneous results."

Daniel Engberg, Head of Data Analytics and Artificial Intelligence, Scandinavian Airlines

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Scandinavian Airlines

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