Microsoft Azure's machine learning and artificial intelligence services use a variety of algorithms and methodologies, including deep learning, decision trees, and support vector machines, among others. These algorithms are designed to analyze and learn from large datasets to make predictions and decisions.
Regarding data privacy and ethical considerations, Microsoft emphasizes the importance of protecting user data and complying with privacy laws. Azure Machine Learning enables administrators and developers to create a secure configuration that complies with their companies' policies. With Azure Machine Learning and the Azure platform, users can restrict access to resources and operations by user account or group, restrict incoming and outgoing network communications, encrypt data in transit and at rest, scan for vulnerabilities, and apply and audit configuration policies. Microsoft has also created two open-source packages that can enable further implementation of privacy and security principles: SmartNoise and Counterfit.
While Microsoft provides tools and guidelines for responsible and trusted AI, the specific algorithms and methodologies used in Azure's machine learning and AI services are not disclosed publicly.