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DATA EQUALITY
Digital data to identify, prevent and counter intersectional discrimination in Phygital AI-based environments
An initiative focused on addressing data-driven discrimination.
Operated by: KMOP

CHALLENGE

DATA EQUALITY aims to prevent data-driven discrimination by developing a standardized methodology for Civil Society Organizations and Judicial bodies/Law Enforcement Agencies
Numerous stakeholders have stressed the urgent need to collect equality data in a coherent, unbiased, and comprehensive way. Despite the E.U.’s strong legal framework promoting equality and non-discrimination, there remains a persistent lack of comparable and consistent data in this area. The European Parliament has highlighted the importance of addressing “under-recording” and “under-reporting” by enhancing the knowledge and skills of judicial and law enforcement officials in handling reports and referrals of racially motivated crimes, particularly in accurately identifying and documenting incidents. Additionally, the E.U. Council has acknowledged the need for more research and data on discrimination, hate speech, and radicalization.

The Project

DATA EQUALITY - Digital data to identify, prevent and counter intersectional discrimination in Phygital AI-based environments

GA Number

101094241

Project Status

Ongoing

Funded by

CERV

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Title that explicitly says that this is a collective work

Document

Report on Equality Data Collection

The report examines how civil society organizations (CSOs) and law enforcement/judicial bodies handle data related to discrimination. It looks at how they collect, manage, analyze, and share this data, highlighting the main problems, gaps, and needs they face, as well as good practices. Additionally, the report evaluates how AI and OSINT (Open-Source Intelligence) tools are used to prevent bias when working with this data.
Document

European Handbook on Equality Data

The handbook explains a new method that aims to help civil society organizations (CSOs) and law enforcement agencies (LEAs) to better collect, manage, analyze, share, and spread information about discrimination. This method aims to make the data more effective, easier to work with, and more standardized so that everyone can use it more effectively. The handbook also includes a special section that outlines how to use AI and OSINT (Open Source Intelligence) tools in a way that avoids bias when processing data and helps identify any biased information.
Web

Capacity-building activities for members of CSOs and LEAs/Judicial bodies

The capacity-building activities aim to train members of CSOs and LEAs/Judicial bodies on using and applying the new methodology to improve, manage, analyze, exchange, and disseminate data on discrimination.

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FUNDED BY

Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or CERV. Neither the European Union nor the granting authority can be held responsible for them.

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