According to recent European legislation, high-risk AI systems must adapt to comply with requirements related to specific areas such as risk management, data quality and governance, logging and traceability, technical documentation, transparency, human oversight, and accuracy, as outlined in the European Artificial Intelligence (AI) Act.
As the standardization process for AI is expected to remain iterative, and currently, there are no European standards fully covering the challenges of algorithmic hiring, we propose specific standardization-oriented recommendations related to the relevant AI areas specified by the European Commission.
For each of these areas, we set the context by describing the requirements that AI systems in high-risk domains, especially in recruitment, should fulfill, as well as the activities that should be carried out to ensure their appropriate use and desired performance, in line with the requirements derived from the AI Act.
Unlike existing horizontal approaches to AI governance and standardization, this paper contributes a vertical, domain-specific framework for algorithmic hiring, especially ranking-based recruitment systems, by mapping the requirements of the AI Act to concrete standardization recommendations, focusing on lifecycle discrimination risks, fairness-aware data governance, explainability, human oversight, and post-deployment monitoring in recruitment systems.
Although our recommendations were informed by the outcomes of the European project FINDHR, they are not tied to the project's technical artifacts and could be implemented using alternative methods, tools, or governance mechanisms.
Blogger's Review: This study provides practical standardization recommendations for high-risk AI systems, particularly in the recruitment sector, highlighting the importance of algorithm transparency and human oversight, which underscores the necessity of adaptability and compliance in a rapidly evolving AI landscape.