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TensorFlow Responsible AI Guidebook


In 2018, Google introduced its AI Principles, which guide the ethical development and use of AI in our research and products. In line with these principles, the TensorFlow team works to provide developers with tools and techniques to adhere to Responsible AI (RAI) practices.

In this guidebook, you’ll find guidance on how to apply tools in the Responsible AI Toolkit to develop a cohesive workflow that serves your specific use case and product needs. Tools in this guidebook include those that can be applied in domains such as fairness and transparency. This is an active area of development at Google, and you can expect this guidebook to include guidance for additional related areas, such as privacy, explainability, and robustness.

Guidebook Organization

API Documentation & Guidance

For each tool, we will provide guidance around what the tool does, where in your workflow it might fit, and its various usage considerations. Where applicable we’ll include an “Install” page in the “Guide" tab for each tool, and detailed API documentation in the "API" tab. For some tools, we’ll also include technical guides that demonstrate concepts that users might find challenging when applying them.


Whenever possible, we’ll provide notebook tutorials showing how tools in the RAI Toolkit can be applied. These are typically toy examples chosen to cast a spotlight on a specific tool. If you have questions about these, or if there are additional use cases you’d like to see explored in tutorials, please reach out to us at

Additional Considerations

Designing a responsible AI workflow requires a thoughtful approach at each stage of the ML lifecycle, from problem formulation to deployment and monitoring. Beyond the details of your technical implementation, you will need to make a variety of sociotechnical decisions in order to apply these tools. Some common RAI considerations that ML practitioners need to make include:

  • Across which demographic categories do I need to ensure my model performs well?
  • If I must store sensitive labels in order to perform fairness evaluation, how should I consider the tradeoff between fairness and privacy?
  • What metrics or definitions should I use to evaluate for fairness?
  • What information should I include in my model and data transparency artifacts?

The answers to these and many other questions depend on your specific use case and product needs. As such, we cannot tell you exactly what to do, but will provide guidance for making responsible decisions, with helpful tips and links to relevant research methods whenever possible. As you develop your responsible AI workflow with TensorFlow, please provide feedback at Understanding your learnings and challenges is critical to our ability to build products that work for everyone.