LAAF: A Layered Accountability Architecture Framework for LLM Applications
作者: Prachi Chaturvedi, Shahnawaz Ahmad, Ehsan Nowroozi, Muhammad Waqas, George Loukas, Alireza Jolfaei, Lucas Cordeiro, Pierre Dantas
分类: cs.AI, cs.CR
发布日期: 2026-08-27
💡 一句话要点
提出LAAF框架以解决大语言模型应用中的问责问题
🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)
关键词: 大语言模型 问责机制 技术控制 人类监督 组织治理 文档追溯 框架设计 合规性
📋 核心要点
- 核心问题：现有大语言模型应用缺乏明确的问责机制，导致责任追溯困难。
- 方法要点：提出LAAF框架，通过四个维度整合技术控制和人类监督，增强问责能力。
- 实验或效果：通过文献综述，识别出问责机制中的缺口，为未来的标准化提供依据。
📝 摘要（中文）
大语言模型（LLMs）在医院、法庭、银行和公共服务等场景中被广泛应用，其流畅且自信的输出常被视为权威，尽管可能缺乏依据或不准确。当这些输出造成伤害时，责任归属及追踪机制显得尤为重要。本文基于PRISMA指导原则，分析了4512条记录，最终纳入122项主要研究和12份监管标准文件，提出了一个分层的问责架构LAAF，涵盖技术控制、人为监督、组织治理及文档追溯等四个维度，并与欧盟AI法案及其他标准相结合，识别出问责机制中的四个持续性缺口及五个结构性张力。
🔬 方法详解
问题定义：本文旨在解决大语言模型应用中问责机制不明确的问题，现有方法未能有效追踪和解释责任，导致潜在的法律和伦理风险。
核心思路：提出LAAF框架，整合技术控制、人为监督、组织治理和文档追溯四个维度，以建立一个全面的问责体系，确保在模型输出造成损害时能够追溯责任。
技术框架：LAAF框架分为四个层次：来源、应用逻辑、人为监督及治理与补救，交叉考虑可追溯性、角色清晰度和持续监控。每个层次都包含成熟度评估，确保问责机制的有效性。
关键创新：LAAF框架的创新在于其综合性和系统性，首次将问责机制与多个监管标准相结合，填补了现有文献中的空白，尤其是在技术控制与人类监督的结合上。
关键设计：框架中的关键设计包括对技术控制的细化与评估标准的制定，确保在不同应用场景下的适用性和有效性，同时考虑到人类监督的具体实施方式和组织治理的结构。
🖼️ 关键图片
📊 实验亮点
通过对122项主要研究的综合分析，本文识别出问责机制中的四个持续性缺口和五个结构性张力，为未来的研究和政策制定提供了重要的参考依据，推动了大语言模型应用的问责性提升。
🎯 应用场景
该研究的潜在应用领域包括医疗、金融、教育及公共服务等多个行业，能够为大语言模型的合规性和问责性提供指导，促进其在高风险场景中的安全应用。未来，该框架可能推动相关法律法规的制定，提升公众对AI系统的信任。
📄 摘要（原文）
Large Language Models (LLMs) operate in hospitals, courtrooms, banks, and public service desks, where fluent, confident outputs are treated as authoritative even when ungrounded or incorrect. When such an output contributes to harm, who is answerable, and through what mechanisms can responsibility be traced, explained, and acted upon? Following PRISMA guidance, five databases were searched from January 2022 to March 2026 against four review questions; of 4,512 records identified, 122 primary studies were included, together with 12 regulatory and standards documents analysed as primary sources. The review consolidates a sociotechnical account of accountability as an actor-forum relation resolved into five dimensions, and synthesises mechanisms across four families: technical controls, human oversight, organisational governance, and documentation and traceability, each with a maturity assessment. The corpus is read through a four-layer classification device spanning provenance, application logic, human oversight, and governance and redress, cross-cut by traceability, role clarity, and continuous monitoring. Both are mapped onto the EU AI Act, whose high-risk obligations have applied since 2 August 2026, the NIST AI RMF with its Generative AI Profile, ISO/IEC 42001, and sectoral guidance in healthcare, consumer finance, education, and the public sector. Four persistent gaps emerge: under-specification of human oversight, absence of shared accountability metrics, disciplinary disconnection, and limited empirical evaluation, alongside five structural tensions that no surveyed instrument resolves. The review closes by consolidating the classification device into an integrated accountability architecture, LAAF, with cybersecurity aligned to the OWASP LLM Top 10 (2025); it is a synthesis of the surveyed evidence rather than a validated artefact.