New resources and tools to enable product leaders to implement AI responsibly

Brass Contributor

Written by Alysa Taylor, Corporate Vice President of Industry, Apps, and Data Marketing at Microsoft


As AI becomes more deeply embedded in our everyday lives, it is incumbent upon all of us to be thoughtful and responsible in how we apply it to benefit people and society. A principled approach to responsible AI will be essential for every organization as this technology matures. As technical and product leaders look to adopt responsible AI practices and tools, there are several challenges including identifying the approach that is best suited to their organizations, products and market.


Today, at our Azure event, Put Responsible AI into Practice, we are pleased to share new resources and tools to support customers on this journey, including guidelines for product leaders co-developed by Microsoft and Boston Consulting Group (BCG). While these guidelines are separate from Microsoft’s own Responsible AI principles and processes, they are intended to provide guidance for responsible AI development through the product lifecycle. We are also introducing a new Responsible AI dashboard for data scientists and developers and offering a view into how customers like Novartis are putting responsible AI into action.


Introducing Ten Guidelines for Product Leaders to Implement AI Responsibly

Though the vast majority of people believe in the importance of responsible AI, many companies aren’t sure how to cross what is commonly referred to as the “Responsible AI Gap” between principles and tangible actions. In fact, many companies actually overestimate their responsible AI maturity, in part because they lack clarity on how to make their principles operational.


To help address this need, we partnered with BCG to develop “Ten Guidelines for Product Leaders to Implement AI Responsibly”—a new resource to help provide clear, actionable guidance for technical leaders to guide product teams as they assess, design, and validate responsible AI systems within their organizations.


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