Aequitas: Bias and Fairness Audit Toolkit


An open source bias audit toolkit for machine learning developers, analysts, and policymakers to audit machine learning models for discrimination and bias, and make informed and equitable decisions around developing and deploying predictive risk-assessment tools.



An open source machine learning toolkit to help data scientists, machine learning developers, and analysts quickly prototype, build and evaluate end-to-end predictive risk modeling systems for public policy and social good problems.

Data Science for Social Good Summer Fellowship


Full-time summer program to train aspiring data scientists to work on machine learning, data science, and AI projects with social impact in a fair and equitable manner. Working closely with governments and nonprofits, fellows take on real-world problems in education, health, criminal justice, sustainability, public safety, workforce development, human services, transportation, economic development, international development, and more.