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AI FAIRNESS 
GLOBAL LIBRARY

Tools, guides, resources, metrics,

and methodologies to support institutions

transforming AI fairness principles into practice. 

All Resources

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The Word Embeddings Fairness Evaluation Framework

Produced mainly for: Tech teams

Origin: North America

Language: English

Type: Tech tool

Creator: IBM

English

Tech tool

Millennium Institute for Foundational Research on Data (IMFD).

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.

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Fairness Compass

Produced mainly for: Tech teams

Origin: North America

Language: English

Type: Tech tool

Creator: IBM

Europe

English

Guide

Boris Ruf and Marcin Detyniecki (AI Research at AXA)

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.

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NZ Algorithm Charter

Produced mainly for: Tech teams

Origin: North America

Language: English

Type: Tech tool

Creator: IBM

Pacific

English & Maori

Framework

Aotearoa/NZ Government

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.

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Model Cards

Produced mainly for: Tech teams

Origin: North America

Language: English

Type: Tech tool

Creator: IBM

North America

English

Guide or manual

Google

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.

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Ethical Toolkit for Engineering/Design Practice

Produced mainly for: Tech teams

Origin: North America

Language: English

Type: Tech tool

Creator: IBM

North America

English

Tech tool

Markkula Center for Applied Ethics – Santa Clara University

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

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Fairness Flow

Produced mainly for: Tech teams

Origin: North America

Language: English

Type: Tech tool

Creator: IBM

North America

English

Guide or manual

Facebook

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

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Veritas Initiative

Produced mainly for: Tech teams

Origin: North America

Language: English

Type: Tech tool

Creator: IBM

Asia

English

Tech tool

Monetary Authority of Singapore

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.

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Bias in machine learning and ethical implications

Produced mainly for: Tech teams

Origin: North America

Language: English

Type: Tech tool

Creator: IBM

North America

English

Guide

Institute of International Finance

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.

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Fairness Toolkit (LiFT)

Produced mainly for: Tech teams

Origin: North America

Language: English

Type: Tech tool

Creator: IBM

North America

English

Tech tool

LinkedIn

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

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Model Artificial Intelligence Governance Framework Second edition

Produced mainly for: Tech teams

Origin: North America

Language: English

Type: Tech tool

Creator: IBM

Asia

English

Guide or manual

SG:D, IMDA, PDPC (Singapore)

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.

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Ethics & Algorithms Toolkit

Produced mainly for: Tech teams

Origin: North America

Language: English

Type: Tech tool

Creator: IBM

North America

English

Tech tool

GovEx, the City and County of San Francisco, Harvard DataSmart, Data Community DC

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.

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¿Cómo implementar la debida diligencia en derechos humanos en el desarrollo de tecnología? El impacto en la privacidad

Produced mainly for: Tech teams

Origin: North America

Language: English

Type: Tech tool

Creator: IBM

Latin America and the Caribbean

Spanish

Guide

Asociación por los Derechos Civiles. Written by Leandro Ucciferri, with the independent consultants Agustina Bendersky and Denisse Cufré

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

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 This is a live Global Library.

This publication was last updated in August 2022. If you have any resource on AI fairness that has not been published on this  Global Library and you would like for it to be considered, or if you are the creator of a resource published here, and would like to edit the information, please send us an email to info@cminds.co

Disclosures:

The material included in this site is not necessarily endorsed by the World Economic Forum, the Global Future Council on AI for Humanity, C Minds and/or other collaborators.

The readers and/or users of each resource must evaluate each tool for his/her specific intended purpose. This first interation includes only free and publicly available resources.

The intelectual property of all of the resources are owned by the creators of each individual resource.

This material may be shared, provided that it is clearly attributed to its creators. This material may not be used for commercial purposes.

Global Future Council on AI for Humanity,WEF with the support of C Minds

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