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Ai Auditing

Explore 19 projects in the Ai Auditing category of the Civic Tech Field Guide.

19 projects

AI Forensics

AI Forensics is a European non-profit that investigates influential and opaque algorithms. We hold major technology platforms accountable by conducting independent and high-profile technical investigations to uncover and expose the harms caused by their algorithms.

Italy
2021

The Workers' Algorithm Observatory (WAO)

Worker-led audits in the platform economy

Princeton University, Princeton, New Jersey, USA
2022
labor

Eticas Foundation

Community-led AI Audits: Making AI Systems Fair & Accountable

New York City
CTFG partners

Eticas Audit library

An open-source Python library designed to assist developers in calculating fairness metrics and assessing bias in machine learning models.

2025

The Observatory of Algorithms with Social Impact (OASI)

The OASI Register compiles information about algorithms with the aim of increasing public awareness and providing the necessary knowledge for an informed public conversation, and of making possible for experts and the public to search and analyse the use and social impact of algorithmic systems across the world.

2021

Automating (In)Justice: An Adversarial Audit of RisCanvi

Eticas Foundation's adversarial audit of an AI-powered criminal justice tool, RisCanvi, that has been used in Catalonia since 2009 finds it "does not meet the required standards of reliability and fairness."

Catalonia (Spain)
criminal justice, recidivism, reports

ADL AI Index

The ADL AI Index evaluates leading large language models (LLMs) on their ability to detect and counter antisemitic and extremist tropes and narratives.

New York City
2026

Humane Intelligence

Humane Intelligence is a tech nonprofit building a community of practice around algorithmic evaluations.

USA

MLCommons AI Safety

The MLCommons AI Safety working group is composed of a global consortium of industry leaders, practitioners, researchers, and civil society experts committed to building a harmonized approach to AI safety.