Responsible AI

AI is transforming industries and solving important, real-world challenges at scale. This vast opportunity carries with it a deep responsibility to build AI that works for everyone.

Staying ahead of the curve: The business case for responsible AI

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Benefits

How values-based AI is good for your business

Safer and more accountable products

Advanced technologies are most successful when everyone can benefit from them. Evaluating your AI systems, both when they perform as intended and when they don’t, is crucial to building accountable products.

Earn and keep your customers' trust

Lack of trust in AI systems is a growing barrier to adoption in enterprise with more organizations selecting enterprise products based on AI commitments and practices. A responsible AI approach earns trust.

A culture of responsible innovation

Empowering AI decision-makers and developers to take ethical considerations into account enables them to find new, innovative ways to drive your mission forward.

Learn more about our perspectives on issues and AI governance at Google and how we work to build responsible AI for everyone.

Key features

Google Cloud’s approach to responsible AI

AI Principles

Since 2018, Google’s AI Principles have served as a living constitution, keeping us motivated by a common purpose. Our center of excellence, the Responsible Innovation team, guides how we put these principles to work company-wide, and informs Google Cloud’s approach to building advanced technologies, conducting research, and drafting our policies.

Putting principles into practice

Rigorous evaluations are a critical component of building successful AI. To drive alignment with our AI Principles at Google Cloud, two diverse review bodies undertake deep ethical analyses and risk and opportunity assessments for any technology product we build and early-stage deals involving custom work. Learn more.

Tools and education

Responsible AI tools are an increasingly effective way to inspect and understand AI models. We’re building resources like Explainable AI, Model Cards, and the TensorFlow open-source toolkit to provide model transparency in a structured, accessible way. We share what we’re learning through Responsible AI practices, fairness best practices, technical references and tech ethics materials.

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