Mainly focused on explaining decisions made with AI, but it contains fairness issues in the model

Helps you structure ideas about the ethical implications of the projects you are working on, to visualize them and to resolve them. Produced mainly for Managers

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.

Tool developed by Cognitive Scale for data scientists to evaluate their AI models for robustness, fairness, and explainability, and allows users to compare different models or model versions for these qualities.

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.

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.

It's more an educational publication than a tool (as the name suggests: "Review of.."). However, also provides some (high-level) recommendations for governments and regulators.

Identifies specific verticals and areas of interest and helps provide highly granular and pragmatic papers and insights as a natural evolution of our work. 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.

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 risk chain model (RCModel) supports AI service providers in proper risk assessment and control, and offers rpolicy recommendations

This toolkit includes resources for advocates interested in or currently engaged in work to uncover where algorithms are being used and to create transparency and accountability mechanisms. Produced mainly for Tech teams.