Barcelona Activa's AI hiring pipeline looked fair in aggregate. A stage-by-stage evaluation found five disparities hiding underneath. Here's the methodology.

A public employment agency in Europe relies on a third-party algorithmic platform to shortlist candidates for job vacancies, a system classified as high-risk under the EU AI Act. Eticas.ai conducted an independent, post-deployment fairness and bias evaluation of that system across five years of operational data. The evaluation found systematic disparities in shortlisting outcomes by gender, age, education level, and national origin — including adverse impact for women in mid-salary roles and the near-total exclusion of candidates aged 55 and over. Eticas.ai delivered targeted recommendations to address the findings.

The FINDHR project introduced practical tools, guidelines, and auditing frameworks designed to reduce discrimination in AI-assisted hiring. These resources support more transparent, inclusive, and accountable recruitment practices across Europe.

Since 2023, New York City's Local Law 144 has required employers to run independent bias audits on any automated employment decision tool (AEDT) used in hiring. At Eticas.ai, we built ITACA_144 — a version of our broader bias-auditing platform, ITACA_OS — to help employers meet this requirement. Along the way, we surfaced several structural gaps in the law that limit how effective it can be, and we believe those lessons are relevant well beyond New York.