Engages in cutting-edge research that can improve and power new product experiences at a huge scale for our community. Building on Facebook AI's key principles of openness, collaboration, excellence, and scale, we make big, bold research investments focused on building social value and bringing the world closer together. Produced mainly for: Non - tech teams

Allows you to flag protected features in your dataset and then actively guides you through the selection of the best fairness metric to fit the specifics of your use case. Produced mainly for Tech teams.

The paper offers concrete guidance to decision-makers in organizations developing and using AI on how to incorporate values into algorithmic decision-making, and how to measure the fulfillment of values using criteria, observables and indicators combined with a context dependent risk assessment.

An algorithmic audit identifies, addresses and corrects algorithmic bias so that you can act on informed and vetted AI-powered business recommendations.

Produced mainly for Companies using AI

The purpose of this manual is to provide technical recommendations and technical best practices in order to avoid contrary results (often unexpected) to the decision-makers' expectations. Produced mainly for Tech teams.

Open source development tool for building simple simulations that explore the potential long-run impacts of deploying machine learning-based decisión systems.

Linkedin - open source software library designed to enable the measurement of fairness in AI and machine learning workflows.

Designed to support teams in evaluating, improving, and comparing models for fairness concerns in partnership with the broader Tensorflow toolkit. Perspective AI is provided as a content moderation case study.

Findings from work by a group of partners in a project to explore the implementation of responsible AI principles, including Deutsche Bank, Linklaters, Microsoft, Standard Chartered, and Visa. Produced mainly for Managers & C levels.

The Guidelines for the Quality Assurance of AI Systems offers a comprehensive technical assessment of quality measures for AI systems, but it is not strictly speaking a document on AI Fairness. It is updated periodically in its original Japanese version, but an informal English translation is available too.

Open-source package for training interpretable (explainable) ML models

Provides detailed and readily-implementable guidance to private sector organisations to address key ethical and governance issues when deploying AI solutions. Produced mainly for Managers & C levels.