The IDC Marketscape advises, “Consider Microsoft when you need a robust and scalable AI governance solution. Microsoft tackles challenges in AI such as transparency, accountability, fairness, reliability, safety, and inclusivity. It provides tools and frameworks for transparency, mechanisms for accountability, techniques to detect and mitigate bias, best practices for reliability, safety measures, and inclusive design principles. Microsoft also offers extensive support services, certifications, workshops, and educational materials. If you want a comprehensive solution with strong expertise, resources, and support, Microsoft is a compelling choice for AI governance.”
At Microsoft, we think about AI governance as encompassing policies, practices, and tools that enable organizations to deploy AI systems in a safe, responsible, and effective way. In other words, it is the “how,” or implementation and operationalization, of responsible AI. For us, that means grounding research, policy, and engineering efforts in our six AI principles and building tools and practices like Azure AI Content Safety, Azure AI prompt flow, and the responsible AI dashboard that help integrate those principles into everyday work. After all, principles are not self-executing. This is why we’re focused on building practical tools and controls to help our customers incorporate their own responsible data and AI policies and practices into each stage of the AI development lifecycle—for improved safety and compliance.
Azure AI helps customers scale AI innovation with confidence
According to IDC’s October 2023 Global AI Buyer Sentiment, Adoption, and Business Value Survey, “cost, lack of skilled staff, and lack of AI governance and risk management solutions” are the top barriers for AI adoption. To adapt and thrive in the era of AI, organizations need to adopt a comprehensive and proactive approach to data and AI governance, inclusive of policies, practices, and integrated tools that support safe and responsible AI at each step of AI development.
Microsoft offers a myriad of data and AI capabilities to help you build, deploy, and manage generative AI and traditional ML solutions with confidence. For example, Azure AI Studio features like prompt flow, Azure AI Content Safety, and model monitoring help teams infuse responsible AI into their LLMOps practices. Azure Machine Learning integrates with Microsoft Purview, empowering organizations to responsibly discover, audit, and manage the data needed to build and deploy AI models, while the Responsible AI dashboard helps them assess and debug models and generate model scorecards as part of their MLOps. Azure AI Studio and Azure Machine Learning also have native integrations with Microsoft Fabric to help customers harness the full potential of their data estate with visibility and control.
Siemens saw a need to enable better cross-functional communication for industrial companies that use its software, allowing those customers to rapidly address problems as they arose on their shop floors. Siemens’ new solution uses Azure AI with translation enabling workers on the shop floor to speak their own native language to describe an observed issue. The system automatically creates a summarized problem report and routes it to the appropriate design, engineering, or manufacturing experts—in any language they prefer. Siemens noted that network isolation and its service-level agreement–backed availability were key in meeting their enterprise grade objectives, and the UI-first approach in prompt flow helped streamline LLMOps.
ERM, the largest global pure play sustainability consultancy, has built a software-as-a-service (SaaS) tool that can rate companies based on their environmental, social, and governance (ESG) performance for private capital investors. Powered by Azure AI, ESG Fusion can provide a comprehensive assessment of a company’s ESG risks and opportunities within two business days—a big step in promoting sustainable business practices around the globe. The company uses the Azure Machine Learning responsible AI dashboard for text for model debugging and visualizations to be able to digest and visualize text data more easily. The dashboard provides several mature tools in the areas of error analysis, model interpretability, unfairness assessment and mitigation for a holistic assessment, debugging of NLP models to make informed business decisions.
Shell and the Department of Education of Southern Australia are helping to protect end users from the classroom to the chatroom using Azure AI Content Safety. The service works by running both the prompt and completion for a generative AI model through classification models aimed at detecting and preventing the output of unwanted and adversarial content, including jailbreaks and protected material. Internally, Microsoft has relied on Azure AI Content Safety to help protect users of its own AI-powered products. The technology was essential to releasing chat-based innovations in products like Bing, GitHub Copilot, Microsoft 365 Copilot, and Azure Machine Learning responsibly.
Providence recognized that the use of large language models presents both opportunities and challenges in the healthcare setting. When building a solution to triage the deluge of electronic messages from patients, they chose Azure OpenAI Service and used the models as a document classifier, which lends itself to a rules-based verification process and minimizes the risks present in other applications of LLMs. They believe this approach—AI with the safeguard of rules—represents a responsible use of AI in healthcare. Now, Providence can quickly and securely classify incoming messages, direct them to the appropriate caregiver, and free providers to focus on patient care.
Swift, a leading infrastructure provider for financial messaging services, has long worked with its community of over 11,500 institutions to drive new ways to detect and catch fraudulent transactions that can cost hundreds of billions annually. Using federated learning techniques along with Azure Machine Learning and Azure confidential computing, Swift and Microsoft are building an anomaly detection model for transactional data—all without copying or moving data from secure locations. The shared vision is that the model will become the new standard for reducing financial crime while achieving the highest level of security, privacy, and cost efficiency.
These are some of the customers who are leveraging Microsoft capabilities to build and scale AI applications responsibly. We continue to innovate with AI to help customers drive AI transformation safely.
Build on a trusted foundation
Microsoft Azure is a trusted platform for AI innovation, offering governance capabilities that help you build AI solutions that scale. By choosing Microsoft Azure, you can benefit from Microsoft’s strong vision and expertise in AI, as well as our extensive experience in AI research and innovation. Whether you are a beginner or an expert in AI, Microsoft Azure can help you accelerate AI adoption that aligns with your organizational values and earns customers’ trust.
Source: microsoft.com
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