---
title: "Vertical Standardisation for High-Risk AI Systems: A Domain-Specific Framework for Algorithmic Hiring"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.12588v1"
permalink: "https://feed.astralor.com/items/itm_b63ab6f80aa57f664628e1c3"
published: "2026-07-14T10:00:27.000Z"
score: 81
channel: "ai"
tags: ["AI Governance","Algorithmic Hiring","EU AI Act","FINDHR","Standardisation"]
---

# Vertical Standardisation for High-Risk AI Systems: A Domain-Specific Framework for Algorithmic Hiring

## AI commentary

> This paper is a must-read for anyone involved in AI governance, HR tech compliance, or product development for the EU market. It goes beyond principles and delivers actionable standardisation recommendations, offering concrete insights into how 'high-risk' translates in hiring scenarios.

## At a glance

This paper proposes a vertical, domain-specific standardisation framework for algorithmic hiring under the EU AI Act. Moving beyond horizontal approaches, it maps regulatory requirements to concrete recommendations covering risk management, data governance, explainability, human oversight, and post-deployment monitoring, with emphasis on discrimination risks and fairness. Recommendations are informed by the FINDHR project but applicable independently.

## Key points

- Proposes a vertical standardisation framework for algorithmic hiring aligned with the EU AI Act
- Covers risk management, data quality, traceability, transparency, and human oversight
- Focuses on lifecycle discrimination risks and fairness-aware data governance in recruitment
- Recommendations informed by FINDHR project but implementable with alternative methods and tools

## Full text

According to the recent European legislation, high-risk AI systems will have to adapt in order to comply with requirements related to specific areas, like 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 standardisation process for AI is expected to remain iterative and, so far, there are no European standards on AI fully covering the challenges of algorithmic hiring, we propose specific standardisation-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, and especially in recruitment, should fulfil, as well as the activities that should be carried out to ensure their appropriate use and desired performance, in line with the requirements deriving from the AI Act. Unlike existing horizontal approaches to AI governance and standardisation, this paper contributes a vertical, domain-specific framework for algorithmic hiring, and especially ranking-based recruitment systems, by mapping the requirements of the AI Act to concrete standardisation recommendations, focusing on lifecycle discrimination risks, fairness-aware data governance, explainability, human oversight, and post-deployment monitoring in recruitment systems. Even though our recommendations were informed by the outcomes of the European project FINDHR, they are not tied to the project's technical artefacts and could be implemented using alternative methods, tools, or governance mechanisms.

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