A discovery process tool for product teams to spot unconscious biases and structural inequalities in AI systems.

Produced mainly for Tech teams.

This extensible open source toolkit can help you examine, report, and mitigate discrimination and bias in machine learning models throughout the AI application lifecycle. We invite you to use and improve it. Produced mainly for Tech teams

Promote use of AI that is innovative and trustworthy and that respects human rights and democratic values. Adopted in May 2019, they set standards for AI that are practical and flexible enough to stand the test of time. Produced mainly for Government and Ministerial Levels.

Addition of industry examples in each section to illustrate how organizations have implemented AI governance practices. Produced mainly for Government

Open source tech tool for auditing the data and predictions of machine learning solutions for their fairness. Requires relatively light technology background (can be used by non-developers)

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.

Participants will learn some of the fundamentals of AI ethics and then immediately get to practice applying ethical considerations related to the principle of Fairness in the context of several humanitarian and international development use cases. Produced mainly for C Level, Manager, Tech Team, Non Tech Team.

Povide AI practitioners and non-technical professionals with the necessary tools to go all the way from foundational basics to advanced application, empowering them to build an AI-powered future. Produced mainly for everyone.

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.

Open sourced bias testing tool useful for developers, that seems active for updating. Produced mainly for Tech teams

The report outlines ways in which financial institutions are cautiously implementing Machine Learning into their processes, such as in credit risk assessment and anti-money laundering, and the mathematical as well as social components of fairness to be considered.
Produced mainly for C leve & Managers.

Philosophical insights aimed at clarifying the nature of artificial intelligence, its relationship with human intelligence, as well as the various types of developments that have taken place in this field and the questions and challenges they raise.