Eticas.ai has been evaluating AI systems in production since 2012. This is where we share what we’ve learned. You’ll find case studies from client work, practical guides on AI governance and evaluation, reports on emerging risks, interviews with practitioners, and a glossary of the concepts that matter most in the field. Whether you build AI, deploy it, or are accountable for its outcomes, this is the evidence base we work from.
Evaluation of an Algorithmic Hiring System: A Public-Sector Case Study
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.
Detecting bias in AI hiring systems
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.