Learn how to integrate Responsible AI practices into your ML workflow using TensorFlow

TensorFlow is committed to helping make progress in the responsible development of AI by sharing a collection of resources and tools with the ML community.

What is Responsible AI?

The development of AI is creating new opportunities to solve challenging, real-world problems. It is also raising new questions about the best way to build AI systems that benefit everyone.

Designing AI systems should follow software development best practices while taking a human-centered
approach to ML


As the impact of AI increases across sectors and societies, it is critical to work towards systems that are fair and inclusive to everyone


Understanding and trusting AI systems is important to ensuring they are working as intended


Training models off of sensitive data needs privacy preserving safeguards


Identifying potential threats can help keep AI systems safe and secure

Responsible AI in your ML workflow

Responsible AI practices can be incorporated at every step of the ML workflow. Here are some key questions to consider at each stage.

Who is my ML system for?

The way actual users experience your system is essential to assessing the true impact of its predictions, recommendations, and decisions. Make sure to get input from a diverse set of users early on in your development process.

Am I using a representative dataset?

Is your data sampled in a way that represents your users (e.g. will be used for all ages, but you only have training data from senior citizens) and the real-world setting (e.g. will be used year-round, but you only have training data from the summer)?

Is there real-world/human bias in my data?

Underlying biases in data can contribute to complex feedback loops that reinforce existing stereotypes.

What methods should I use to train my model?

Use training methods that build fairness, interpretability, privacy, and security into the model.

How is my model performing?

Evaluate user experience in real-world scenarios across a broad spectrum of users, use cases, and contexts of use. Test and iterate in dogfood first, followed by continued testing after launch.

Are there complex feedback loops?

Even if everything in the overall system design is carefully crafted, ML-based models rarely operate with 100% perfection when applied to real, live data. When an issue occurs in a live product, consider whether it aligns with any existing societal disadvantages, and how it will be impacted by both short- and long-term solutions.

Responsible AI tools for TensorFlow

The TensorFlow ecosystem has a suite of tools and resources to help tackle some of the questions above.

Step 1

Define problem

Use the following resources to design models with Responsible AI in mind.

People + AI Research (PAIR) Guidebook

Learn more about the AI development process and key considerations.

PAIR Explorables

Explore, via interactive visualizations, key questions and concepts in the realm of Responsible AI.

Step 2

Construct and prepare data

Use the following tools to examine data for potential biases.

Know Your Data (Beta)

Interactively investigate your dataset to improve data quality and mitigate fairness and bias issues.

TF Data Validation

Analyze and transform data to detect problems and engineer more effective feature sets.

Data Cards

Create a transparency report for your dataset.

Monk Skin Tone Scale (MST)

A more inclusive skin tone scale, open licensed, to make your data collection and model building needs more robust and inclusive.

Step 3

Build and train model

Use the following tools to train models using privacy-preserving, interpretable techniques, and more.

TF Model Remediation

Train machine learning models to promote more equitable outcomes.

TF Privacy

Train machine learning models with privacy.

TF Federated

Train machine learning models using federated learning techniques.

TF Constrained Optimization

Optimize inequality-constrained problems.

TF Lattice

Implement flexible, controlled, and interpretable lattice-based models.

Step 4

Evaluate model

Debug, evaluate, and visualize model performance using the following tools.

Fairness Indicators

Evaluate commonly-identified fairness metrics for binary and multi-class classifiers.

TF Model Analysis

Evaluate models in a distributed manner and compute over different slices of data.

What-If Tool

Examine, evaluate, and compare machine learning models.

Language Interpretability Tool

Visualize and understand NLP models.

Explainable AI

Develop interpretable and inclusive machine learning models.

TF Privacy Tests

Assess the privacy properties of classification models.


Measure and visualize the machine learning workflow.

Step 5

Deploy and monitor

Use the following tools to track and communicate about model context and details.

Model Card Toolkit

Generate model cards with ease using the Model Card toolkit.

ML Metadata

Record and retrieve metadata associated with ML developer and data scientist workflows.

Model Cards

Organize the essential facts of machine learning in a structured way.

Community resources

Learn what the community is doing and explore ways to get involved.

Crowdsource by Google

Help Google's products become more inclusive and representative of your language, region and culture.

Responsible AI DevPost Challenge

We asked participants to use TensorFlow 2.2 to build a model or application with Responsible AI principles in mind. Check out the gallery to see the winners and other amazing projects.

Responsible AI with TensorFlow (TF Dev Summit '20)

Introducing a framework to think about ML, fairness and privacy.