The IIF Data Ethics Charter outlines a set of principles for the ethical handling of customer data in the financial services industry and larger economy. Principles and examples of practice provide an overview of how financial institutions responsibly manage, protect, share, and use customer data.

A guide and toolkit for broader public and business leaders to consider not only AI fairness but also business strategy, governance and responsibility.

Santa Clara University - Multi set of tools implementing ethical reflection, deliberation, and judgment into engineering and design workflows.

A practical toolkit for cities to use to help them identify the risks of using an AI algorithm, and maps out the mitigating measures for different risks.

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

Enables developers or auditing entities to discover and test for unwarranted associations between an algorithm's outputs and certain user subpopulations identified by protected features. Produced mainly for Tech teams

The pou define why we exist, who we serve, what our goals are and how we make good decisions as kaitiaki for whānau. Produced mainly for Non - Tech teams.

For visualizing and anticipating future risk of technology products, acknowledging that once technology is released and reaches scale it may be used for purposes beyond the original intention.

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.

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

Summarices the main themes in the submissions we received and, where appropriate, provide our responses. Provides further clarification on how we will apply our Framework in practice.

Common definitions of fairness and ways of calculating the performance of a machine learning model. Mathematical tension across different fairness definitions makes it impossible to achieve "complete fairness." It helps stakeholders identify the most appropriate fairness definition for a specific use case via a decision tree. Produced mainly for Managers, Tech teams & Non - tech teams.