# TensorFlow Responsible AI Guidebook

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## Introduction

In 2018, Google introduced its [AI
Principles](https://www.blog.google/technology/ai/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.

### Tutorials

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](https://www.tensorflow.org/tutorials),
please reach out to us at [tf-responsible-ai@google.com](mailto:tf-responsible-ai@google.com).

### 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?

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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 [tf-responsible-ai@google.com](mailto:tf-responsible-ai@google.com).
Understanding your learnings and challenges is critical to our ability to build
products that work for everyone.