针对招聘类高风险AI,提出符合欧盟AI法案的垂直领域标准化框架。
Vertical Standardisation for High-Risk AI Systems under the EU AI Act: A Domain-Specific Framework for Algorithmic Hiring
- 构建面向招聘场景的垂直标准框架,覆盖全生命周期公平性风险。
- 将法案要求转化为可操作的数据治理、可解释性与人工监督建议。
- 适用于人力资源科技公司及合规团队,助力算法招聘系统落地。
根据最新欧盟立法,高风险AI系统需满足风险管控、数据质量、治理、日志追溯、技术文档、透明度、人工干预和准确性等要求。由于当前尚无全面覆盖算法招聘挑战的欧洲标准,本文针对欧盟委员会明确的AI领域,提出具体的标准制定建议。针对招聘等高风险场景,梳理系统应满足的合规要求及保障适当使用与性能的实施活动。区别于现有横向治理模式,本研究提出面向算法招聘(尤其是排序型招聘系统)的垂直领域框架,将欧盟AI法案要求映射为具体标准建议,聚焦生命周期歧视风险、公平感知的数据治理、可解释性、人工监督及部署后监控。虽参考了欧洲FINDHR项目成果,但不绑定其技术实现,可适配其他方法或治理机制。
原文摘要 · Abstract (English)
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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