Responsible AI (RAI) is the process of designing, building, and using AI to help employees, businesses, customers, and society as a whole. In addition, RAI enables businesses to build trust and confidently scale AI.

RAI is the only way to reduce the risks that come with AI. Now is the time to look at your current practices or make new ones so you can build technology and use data responsibly and ethically and be ready for future rules. Furthermore, Responsible AI toolkits can create robust, transparent, and fair AI applications and systems. We have compiled a list of resources and toolkits to support the implementation of RAI.

AI Fairness 360

The IBM AI Fairness 360 toolkit is an open-source library that can be expanded and contains methods created by the research community. As a result, bias in machine learning models is detected and reduced throughout the lifecycle of an AI application.

Fairlearn

Fairlearn enables AI system developers to assess the fairness of their systems and address any issues discovered. It includes mitigation algorithms as well as model evaluation metrics.

Model Card Toolkit

The Model Card Toolkit (MCT) makes it easier and faster to make Model Cards. Model Cards are documents for machine learning that explain how we built a model and how well it works.

Responsible AI Toolbox

Microsoft's Responsible AI Toolbox is a collection of user interfaces for model and data exploration and assessment that aid in understanding AI systems. The technique can be evaluated, developed, and used in AI systems safely, safely, and ethically.

TensorFlow Model Remediation

The Model Remediation library provides solutions for machine learning practitioners looking to reduce or eliminate user harm caused by underlying performance biases when creating and training models.

TensorFlow Federated

TensorFlow Federated (TFF) is a free and open-source machine learning framework. TFF was founded to facilitate open research and Federated Learning (FL) experimentation. TFF enables developers to experiment with new algorithms and simulate the federated learning algorithms on their models and data. Non-learning computations such as federated analytics using TFF's building blocks.

TensorFlow Privacy

Tensorflow Privacy (TF Privacy) is a Google Research open-source library. The library includes TensorFlow Optimizer implementations for training ML models with DP. The goal is to allow ML practitioners to train privacy-preserving models using standard Tensorflow APIs with only a few lines of code change. Furthermore, we can use private optimizers in conjunction with high-level APIs that use the Optimizer class, particularly Keras. The API documentation includes details on all of the Optimizers and models.

TextAttack

TextAttack is a Python framework for natural language processing (NLP), adversarial attacks, training, and data augmentation. TextAttack makes it simple, quick, and painless to test the robustness of NLP models.

XAI

XAI enables ML engineers and domain experts to analyze the end-to-end solution and identify discrepancies that could lead to sub-optimal performance.