Vertical Standardisation for High-Risk AI Systems under the EU AI Act: A Domain-Specific Framework for Algorithmic Hiring

• URL: http://arxiv.org/abs/2607.12588v1
• Date: Tue, 14 Jul 2026 10:00:27 GMT
• Title: Vertical Standardisation for High-Risk AI Systems under the EU AI Act: A Domain-Specific Framework for Algorithmic Hiring
• Abstract summary: This paper contributes a vertical, domain-specific framework for algorithmic hiring and especially ranking-based recruitment systems.<n>It focuses on lifecycle discrimination risks, fairness-aware data governance, explainability, human oversight, and post-deployment monitoring in recruitment systems.
• Score: 0.825845106786193
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• Standards for trustworthy AI in the European Union: technical rationale, structural challenges, and an implementation path [0.0]
  This white paper examines the technical foundations of European AI standardization under the AI Act.<n>It explains how harmonized standards enable the presumption of conformity mechanism, describes the CEN/CENELEC standardization process, and analyzes why AI poses unique challenges.
  arXiv  Detail & Related papers  (2026-01-21T11:58:47Z)
• Lost in Vagueness: Towards Context-Sensitive Standards for Robustness Assessment under the EU AI Act [2.740981829798319]
  Robustness is a key requirement for high-risk AI systems under the EU Artificial Intelligence Act (AI Act)<n>This paper investigates what it means for AI systems to be robust and illustrates the need for context-sensitive standardisation.
  arXiv  Detail & Related papers  (2025-11-19T17:06:36Z)
• Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned [45.44933002008943]
  This white paper presents the T"UV AUSTRIA Trusted AI framework.<n>It is an end-to-end audit catalog and methodology for assessing and certifying machine learning systems.<n>Building on three pillars - Secure Software Development, Functional Requirements, and Ethics & Data Privacy - it translates the high-level obligations of the EU AI Act into specific, testable criteria.
  arXiv  Detail & Related papers  (2025-09-08T17:52:08Z)
• Watermarking Without Standards Is Not AI Governance [46.71493672772134]
  We argue that current implementations risk serving as symbolic compliance rather than delivering effective oversight.<n>We propose a three-layer framework encompassing technical standards, audit infrastructure, and enforcement mechanisms.
  arXiv  Detail & Related papers  (2025-05-27T18:10:04Z)
• Compliance of AI Systems [0.0]
  This paper systematically examines the compliance of AI systems with relevant legislation, focusing on the EU's AI Act.<n>The analysis highlighted many challenges associated with edge devices, which are increasingly being used to deploy AI applications closer and closer to the data sources.<n>The importance of data set compliance is highlighted as a cornerstone for ensuring the trustworthiness, transparency, and explainability of AI systems.
  arXiv  Detail & Related papers  (2025-03-07T16:53:36Z)
• AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons [62.374792825813394]
  This paper introduces AILuminate v1.0, the first comprehensive industry-standard benchmark for assessing AI-product risk and reliability.<n>The benchmark evaluates an AI system's resistance to prompts designed to elicit dangerous, illegal, or undesirable behavior in 12 hazard categories.
  arXiv  Detail & Related papers  (2025-02-19T05:58:52Z)
• COMPL-AI Framework: A Technical Interpretation and LLM Benchmarking Suite for the EU Artificial Intelligence Act [40.233017376716305]
  The EU's Artificial Intelligence Act (AI Act) is a significant step towards responsible AI development.<n>It lacks clear technical interpretation, making it difficult to assess models' compliance.<n>This work presents COMPL-AI, a comprehensive framework consisting of the first technical interpretation of the Act.
  arXiv  Detail & Related papers  (2024-10-10T14:23:51Z)
• Navigating the EU AI Act: A Methodological Approach to Compliance for Safety-critical Products [0.0]
  This paper presents a methodology for interpreting the EU AI Act requirements for high-risk AI systems. We first propose an extended product quality model for AI systems, incorporating attributes relevant to the Act not covered by current quality models. We then propose a contract-based approach to derive technical requirements at the stakeholder level.
  arXiv  Detail & Related papers  (2024-03-25T14:32:18Z)
• How VADER is your AI? Towards a definition of artificial intelligence systems appropriate for regulation [39.58317527488534]
  Recent AI regulation proposals adopt AI definitions affecting ICT techniques, approaches, and systems that are not AI.<n>We propose a framework to score how validated as appropriately-defined for regulation (VADER) an AI definition is.
  arXiv  Detail & Related papers  (2024-02-07T17:41:15Z)
• The risks of risk-based AI regulation: taking liability seriously [46.90451304069951]
  The development and regulation of AI seems to have reached a critical stage. Some experts are calling for a moratorium on the training of AI systems more powerful than GPT-4. This paper analyses the most advanced legal proposal, the European Union's AI Act.
  arXiv  Detail & Related papers  (2023-11-03T12:51:37Z)
• Towards an AI Accountability Policy [16.59829043755575]
  We examine how high-risk technologies have been successfully regulated at the national level.<n>We propose a tiered system of explainability and benchmarking requirements for commercial AI systems.
  arXiv  Detail & Related papers  (2023-07-25T17:09:28Z)
• An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
  This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
  arXiv  Detail & Related papers  (2021-05-07T12:01:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.