Open source library for measuring bias in word embedding models. It generalizes many existing fairness metrics into a unified framework and provides a standard interface for: encapsulating existing fairness metrics from previous work and designing new ones, encapsulating the test words used by fairness metrics into standard objects called queries and computing a fairness metric on a given pre-trained word embedding model using user-given queries. Produced mainly for Tech teams.

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.

Demonstrates a commitment to ensuring New Zealanders to have confidence in how government agencies use algorithms. The charter is one of many ways that the government demonstrates transparency and accountability in the use of data. Produced mainly for Government agencies.

Short documents accompanying trained machine learning models that provide benchmarked evaluation in a variety of conditions, such as across different cultural, demographic, or phenotypic groups and intersectional groups that are relevant to the intended application domains. Produced mainly for: C levels & Managers.

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

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

Framework for financial institutions to promote the responsible adoption of Artificial Intelligence and Data Analytics. It has developed a fairness assessment methodology in credit risk scoring and customer marketing, and has published whitepapers on the fairness assessment methodology and the open source code of these two use cases. 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.

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

Incorporates the experiences of organizations that have adopted AI, and feedback from our participation in leading international platforms, such as the European Commission’s High-Level Expert Group and the OECD Expert Group on AI. Produced mainly for Manager.

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.

Helps identifying how the technological development may harm people's right to privacy. Either directly by the use of such technology, or indirectly as a result of the use of a third party. Produced mainly for Managers