Responsible AI highlights the importance of using AI for legitimate reasons while avoiding immoral uses. Organizations with transparent ethical AI standards will follow strict guidelines, a thorough review process, and clear goals to ensure all standards are met. Here are the top 10 open-source toolkits to develop responsible AI systems in 2023.
TensorFlow Privacy is a Python library that includes implementations of TensorFlow optimizers for training machine learning models with differential privacy.
TFF has been developed to facilitate open research and experimentation with Federated Learning (FL), an approach to machine learning where a shared global model is trained across many participating clients that keep their training data locally.
Deon is a command-line tool that allows you to easily add an ethics checklist to your data science projects. The goal of Deon is to push that conversation forward and provide concrete, actionable reminders to the developers that have influence over how data science gets done. Federated Learning helps preserve privacy, as it's a new machine learning paradigm to learn a shared model across users or organizations without direct access to the data.
MCT streamlines and automates the generation of Model Cards [1], machine learning documents that provide context and transparency into a model's development and performance.
TensorFlow Model Remediation is a library that provides solutions for machine learning practitioners working to create and train models in a way that reduces or eliminates user harm resulting from underlying performance biases.
The AI Fairness 360 toolkit from IBM is an extensible open-source library containing techniques developed by the research community to help detect and mitigate bias in machine learning models throughout the AI application lifecycle.
Fairlearn is a Python package that empowers developers of artificial intelligence (AI) systems to assess their system's fairness and mitigate any observed unfairness issues. Fairlearn contains mitigation algorithms as well as metrics for model assessment.
From Microsoft, the Responsible AI Toolbox is a suite of tools that provides a collection of model and data exploration and assessment user interfaces that enable a better understanding of AI systems. It's an approach to assessing, developing, and deploying AI systems in a safe, trustworthy, and ethical manner, and taking responsible decisions and actions.
The moDel Agnostic Language for Exploration and eXplanation (aka DALEX) package Xrays any model and helps to explore and explain its behavior, while helping to understand how complex models are working.
TensorFlow Data Validation (TFDV) is a library for exploring and validating machine learning data. It is designed to be highly scalable and to work well with TensorFlow and TensorFlow Extended (TFX).
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