arXivDaily arXiv每日学术速递 周一至周五更新
​通知:联邦节假日调休:哥伦布日 / 原住民日(Columbus Day / Indigenous Peoples' Day),今日(13日)无更新。
arXiv 2503.18994cs.CYcs.AIcs.LG

HH4AI:欧盟人工智能法案下的人工智能人权影响评估方法论框架

HH4AI: A methodological Framework for AI Human Rights impact assessment under the EUAI ACT

Università degli Studi di Milano(米兰大学);Deloitte Financial Advisory S.r.l. S.B.(德勤财务咨询有限责任公司)
Paolo Ceravolo, Ernesto Damiani, Maria Elisa D'Amico, Bianca de Teffe Erb, Simone Favaro, Nannerel Fiano, Paolo Gambatesa, Simone La Porta, Samira Maghool, Lara… 展开作者

Paolo Ceravolo, Ernesto Damiani, Maria Elisa D'Amico, Bianca de Teffe Erb, Simone Favaro, Nannerel Fiano, Paolo Gambatesa, Simone La Porta, Samira Maghool, Lara Mauri, Niccolo Panigada, Lorenzo Maria Ratto Vaquer, Marta A. Tamborini

AI总结:

提出HH4AI方法论,基于欧盟AI法案,通过门控式基本权利影响评估框架,系统评估AI系统人权影响,提升合规性。

摘要:

本文介绍了HH4AI方法论,这是一种评估人工智能系统对人权影响的系统性方法,重点关注对欧盟人工智能法案的合规性,并应对技术、伦理和监管方面的挑战。文章强调了人工智能的变革性本质,其由自主性、数据和目标导向设计驱动,以及欧盟人工智能法案如何促进透明度、问责性和安全性。一个关键挑战是在各行业中定义和评估“高风险”人工智能系统,这因缺乏普遍接受的标准和人工智能的快速发展而变得复杂。\n\n为应对这些挑战,本文探讨了ISO/IEC和IEEE标准的相关性,重点关注风险管理、数据质量、偏见缓解和治理。它提出了一种基本权利影响评估(FRIA)方法论,这是一个基于门控的框架,旨在通过多个阶段隔离和评估风险,包括人工智能系统概述、人权检查清单、影响评估和最终输出阶段。一种过滤机制根据系统特征定制评估,针对问责性、人工智能素养、数据治理和透明度等领域。\n\n本文通过一个虚构的自动化医疗分诊服务案例研究来说明FRIA方法论。这种结构化方法能够实现系统性过滤、全面的风险评估和缓解规划,有效优先处理关键风险并提供明确的补救策略。这促进了与人权原则的更好对齐,并增强了监管合规性。

英文摘要:

This paper introduces the HH4AI Methodology, a structured approach to assessing the impact of AI systems on human rights, focusing on compliance with the EU AI Act and addressing technical, ethical, and regulatory challenges. The paper highlights AIs transformative nature, driven by autonomy, data, and goal-oriented design, and how the EU AI Act promotes transparency, accountability, and safety. A key challenge is defining and assessing "high-risk" AI systems across industries, complicated by the lack of universally accepted standards and AIs rapid evolution. To address these challenges, the paper explores the relevance of ISO/IEC and IEEE standards, focusing on risk management, data quality, bias mitigation, and governance. It proposes a Fundamental Rights Impact Assessment (FRIA) methodology, a gate-based framework designed to isolate and assess risks through phases including an AI system overview, a human rights checklist, an impact assessment, and a final output phase. A filtering mechanism tailors the assessment to the system's characteristics, targeting areas like accountability, AI literacy, data governance, and transparency. The paper illustrates the FRIA methodology through a fictional case study of an automated healthcare triage service. The structured approach enables systematic filtering, comprehensive risk assessment, and mitigation planning, effectively prioritizing critical risks and providing clear remediation strategies. This promotes better alignment with human rights principles and enhances regulatory compliance.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑