DATA EQUALITY
Digital data to identify prevent and counter intersectional discrimination in Phygital AI-based environments
Funded by: CERV
Challenge
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.
Innovation
DATA EQUALITY aims to prevent data-driven discrimination by developing a standardized methodology for Civil Society Organizations (CSOs) and Judicial bodies/Law Enforcement Agencies (LEAs) to collect, manage, analyze, and disseminate unbiased data. This approach considers key factors like gender, ethnicity, race, and minority identities. The methodology incorporates specific mechanisms and technical guidelines to help CSOs and LEAs use AI and Open Source Intelligence (OSINT) to filter out biased data and identify biased information effectively.
