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For Humanity AI Auditing System

Overview  概述

The For Humanity project aims to develop a modern auditing system for AI technologies that streamlines compliance verification while ensuring ethical deployment. For Humanity, an international non-profit organization, is pioneering a new industry standard for "Triple-A systems auditing" [1] – covering Automation, Algorithms, and AI. Their goal is to create a certification process similar to established frameworks like "Heart Tick" or "B Corp" that provides assurance to consumers and businesses that AI systems are transparent, ethical, and compliant with relevant regulations.
For Humanity 项目旨在为 AI 技术开发现代审计系统,在确保合乎道德部署的同时简化合规性验证。国际非营利组织 For Humanity 正在开创“Triple-A 系统审计”[1] 的新行业标准——涵盖自动化、算法和 AI。他们的目标是创建一个类似于“Heart Tick”或“B Corp”等既定框架的认证流程,向消费者和企业保证 AI 系统是透明的、合乎道德的,并且符合相关法规。

[1] As AI technologies become increasingly embedded in critical decision-making environments, Triple-A systems auditing framework emphasizes the need for systematic, transparent, and accountable evaluation across all layers of an intelligent system. It extends beyond traditional IT auditing by incorporating algorithmic transparency, data ethics, model performance, and compliance with regulatory standards
[1] 随着 AI 技术越来越多地嵌入关键决策环境中,Triple-A 系统审计框架强调需要在智能系统的所有层进行系统、透明和负责任的评估。它通过整合算法透明度、数据道德、模型性能和监管标准合规性,超越了传统的 IT 审计

The Problem  问题

Current auditing practices for AI systems remain largely antiquated and ineffective. Audits are conducted using manual processes, often with simple PDF checklists and physical document collection. This approach is not only time-consuming, taking up to six months to complete, but also fails to provide substantive assessment of AI systems' capabilities and limitations. Most concerning is that traditional audits merely verify document existence without evaluating their practical effectiveness or fitness for purpose.
目前对 AI 系统的审计实践在很大程度上仍然过时且无效。审计是使用手动流程进行的,通常使用简单的 PDF 清单和物理文档收集。这种方法不仅耗时,需要长达六个月才能完成,而且无法对 AI 系统的能力和局限性进行实质性评估。最令人担忧的是,传统的审计仅验证文件是否存在,而没有评估其实际有效性或适用性。

The consequences of inadequate AI system auditing can be severe, as demonstrated by the robo-debt scandal where a poorly designed automated algorithm caused significant harm and resulted in criminal charges for multiple individuals. This case highlights the critical need for robust auditing processes that go beyond superficial compliance checks to ensure AI systems are deployed responsibly and ethically.
人工智能系统审计不足的后果可能很严重,机器人债务丑闻就证明了这一点,其中设计不佳的自动化算法造成了重大伤害,并导致多人受到刑事指控。本案例凸显了对稳健审计流程的迫切需求,这些流程超越了肤浅的合规性检查,以确保以负责任和合乎道德的方式部署 AI 系统。

[2] The Robodebt Scandal refers to a controversial and unlawful automated debt recovery program implemented by the Australian Government between 2015 and 2019. Under this scheme, the Department of Human Services used an algorithm to match income data from the Australian Taxation Office (ATO) with Centrelink welfare records to identify alleged overpayments. The system generated debt notices without proper human oversight or verification, often relying on flawed income averaging methods. This resulted in thousands of incorrect debt notices being issued to vulnerable individuals. The program was later ruled unlawful by the Federal Court, leading to a public apology, financial settlements, and a Royal Commission in 2023, which criticized systemic failures in governance, accountability, and ethical oversight.
[2] 机器人债务丑闻是指澳大利亚政府在 2015 年至 2019 年期间实施的一项有争议且非法的自动债务追讨计划。根据该计划,公共服务部使用一种算法将澳大利亚税务局 (ATO) 的收入数据与 Centrelink 福利记录进行匹配,以识别涉嫌超额支付的情况。该系统在没有适当的人工监督或验证的情况下生成债务通知,通常依赖于有缺陷的收入平均方法。这导致向弱势群体发出了数千份不正确的债务通知。该计划后来被联邦法院裁定为非法,导致公开道歉、财务结算和 2023 年皇家委员会,该委员会批评了治理、问责制和道德监督方面的系统性失败。

Why This Matters  为什么这很重要

As AI becomes increasingly integrated into critical systems and decision-making processes, the need for standardized, thorough auditing becomes paramount. Organizations operating in a global economy benefit from adhering to international best practices, even when specific regulations like the EU AI Act [3] may not directly apply in their jurisdiction. Proper auditing provides assurance that AI systems are well-understood, their limitations are acknowledged, and appropriate human oversight is maintained.
随着 AI 越来越多地集成到关键系统和决策流程中,对标准化、全面审计的需求变得至关重要。在全球经济中运营的组织受益于遵守国际最佳实践,即使欧盟人工智能法案 [3] 等特定法规可能不直接适用于其管辖区。适当的审计可以确保 AI 系统得到充分理解,承认其局限性,并保持适当的人工监督。

The current gap in auditing sophistication creates an opportunity to revolutionize how organizations approach compliance and risk management for AI technologies. A modern auditing system would not only streamline the process but also provide more meaningful insights to decision-makers who may lack technical expertise but bear responsibility for AI deployment.
当前审计复杂性的差距为彻底改变组织处理 AI 技术的合规性和风险管理的方式创造了机会。现代审计系统不仅可以简化流程,还可以为可能缺乏技术专业知识但负责 AI 部署的决策者提供更有意义的见解。

[3] The EU Artificial Intelligence Act (EU AI Act) is a pioneering legislative proposal introduced by the European Union to regulate the development, deployment, and use of Artificial Intelligence (AI) systems within its jurisdiction. Its primary objective is to ensure AI technologies are safe, ethical, and respect fundamental rights, by categorizing AI systems into risk levels (e.g., unacceptable, high, limited, and minimal risk) and applying corresponding obligations. It represents the first comprehensive legal framework on AI globally.
[3] 欧盟人工智能法案(欧盟人工智能法案) 是欧盟提出的一项开创性立法提案,旨在规范其管辖范围内人工智能 (AI) 系统的开发、部署和使用。其主要目标是通过将 AI 系统分为风险级别(例如,不可接受、高、有限和最小风险)并适用相应的义务,确保 AI 技术安全、合乎道德并尊重基本权利。它代表了全球第一个关于人工智能的全面法律框架。

The Solution  解决方案

The proposed system will transform the AI auditing process through a user-friendly platform that enables efficient evidence collection, assessment, and reporting. It will allow various stakeholders – from internal teams preparing for audits to external auditors verifying compliance – to collaborate effectively in a digital environment. The platform will support document uploads, structured assessment frameworks, and clear visualization of compliance status.
拟议的系统将通过一个用户友好的平台改变 AI 审计流程,该平台可实现高效的证据收集、评估和报告。它将允许各种利益相关者(从准备审计的内部团队到验证合规性的外部审计师)在数字环境中有效协作。该平台将支持文档上传、结构化评估框架和合规性状态的清晰可视化。

At the core of the platform, a responsive web interface would enable users to interact with a visual grid of audit categories, represented as interactive tiles. Each tile will dynamically reflect progress and compliance status through intuitive visual indicators. Users should be able to click into each category to access detailed checklists, upload supporting documentation via drag-and-drop functionality, and add contextual comments. The backend infrastructure will include a robust database to store audit items, document metadata, user roles, and workflow states. Additionally, the system would incorporate document storage capabilities to securely manage and version control uploaded evidence.
作为平台的核心,响应式 Web 界面将使用户能够与审计类别的可视化网格进行交互,这些网格表示为交互式磁贴。每个磁贴将通过直观的视觉指示器动态反映进度和合规状态。用户应该能够点击进入每个类别以访问详细的清单,通过拖放功能上传支持文档,并添加上下文注释。 后端基础设施将包括一个强大的数据库,用于存储审计项目、文档元数据、用户角色和工作流状态。此外,该系统还将整合文档存储功能,以安全地管理和版本控制上传的证据。

To ensure audit integrity and facilitate effective collaboration, the platform maintains role-based access control allowing different user types (internal auditors, compliance manager, external auditors) to view or act on different sections. Built-in progress tracking and status updates will be automated, supporting real-time feedback and task monitoring. Upon completion of all reviews, the system produces comprehensive reports featuring clear visualizations such as charts and graphs and concise narratives to effectively convey the current compliance status and highlight potential risks. The report is presented to non-technical stakeholders, such as senior management and board members, to help them ultimately make key decisions about AI deployment.
为了确保审计完整性并促进有效协作,该平台维护了基于角色的访问控制,允许不同的用户类型(内部审计员、合规经理、外部审计员)查看或作不同的部分。内置的进度跟踪和状态更新将是自动化的,支持实时反馈和任务监控。完成所有审查后,系统会生成全面的报告,其中包含清晰的可视化内容(如图表和图形)和简洁的叙述,以有效传达当前的合规状态并突出潜在风险。该报告提交给非技术利益相关者,例如高级管理层和董事会成员,以帮助他们最终做出有关 AI 部署的关键决策。

Scope and Timeframe  范围和时间范围

Given the 10-week timeline, this project will focus on comprehensive requirements elicitation and the development of interactive prototypes that address the most pressing needs in AI auditing. Our primary objective is to thoroughly understand stakeholder needs, document detailed requirements, and demonstrate these through functional prototypes before moving to full implementation. While the potential for disruption in the auditing industry is significant, our immediate goal is to design a system that simplifies evidence collection, standardizes assessment processes, and improves reporting clarity.
鉴于 10 周的时间表,该项目将侧重于综合需求获取和交互式原型的开发,以解决 AI 审计中最紧迫的需求。我们的主要目标是彻底了解利益相关者的需求,记录详细的需求,并在全面实施之前通过功能原型来演示这些需求。虽然审计行业可能发生的巨大颠覆性变化,但我们的近期目标是设计一个系统,以简化证据收集、标准化评估流程并提高报告的清晰度。

The requirements and subsequent design will emphasize modularity, allowing for adaptation to various compliance frameworks such as the EU AI Act [3], GDPR [4], and others that may emerge as the field of AI regulation evolves. This approach ensures the solution remains relevant and adaptable as regulatory landscapes change. Through iterative prototyping, we will validate our understanding of user needs and refine the system design before transitioning to implementation.
这些要求和后续设计将强调模块化,从而适应各种合规框架,例如欧盟 AI 法案 [3]、GDPR [4] 以及随着 AI 监管领域的发展而可能出现的其他框架。这种方法可确保解决方案在监管环境发生变化时保持相关性和适应性。通过迭代原型设计,我们将验证我们对用户需求的理解,并在过渡到实现之前完善系统设计。

[4] The General Data Protection Regulation (GDPR), enforced since May 2018, is the EU’s cornerstone legislation on data protection and privacy. It governs the processing of personal data of individuals in the EU and emphasizes transparency, accountability, data minimization, and user consent. GDPR has significantly influenced data governance globally and serves as a benchmark for privacy legislation in other regions.
[4] 自 2018 年 5 月起实施的 《通用数据保护条例》(GDPR) 是欧盟关于数据保护和隐私的基石立法。它管理欧盟个人个人数据的处理,并强调透明度、问责制、数据最小化和用户同意。GDPR 对全球数据治理产生了重大影响,并成为其他地区隐私立法的基准。

Impact  冲击

A successful requirements elicitation and prototyping phase will establish the foundation for a system that significantly reduces the time and effort required for AI audits while improving their effectiveness. With clearly defined requirements and validated prototypes, organizations will be able to envision how the final system will help them gain greater confidence in their compliance status and better understand the risks associated with their AI deployments. Most importantly, well-designed auditing practices will help prevent harmful consequences from flawed AI systems, protecting both organizations and the individuals affected by algorithmic decisions.
成功的需求获取和原型设计阶段将为系统奠定基础,该系统可显著减少 AI 审计所需的时间和精力,同时提高其有效性。通过明确定义的要求和经过验证的原型,组织将能够设想最终系统将如何帮助他们对合规性状态更有信心,并更好地了解与其 AI 部署相关的风险。最重要的是,精心设计的审计实践将有助于防止有缺陷的 AI 系统造成有害后果,从而保护受算法决策影响的组织和个人。

By thoroughly documenting requirements and creating effective prototypes for modernizing the AI auditing process, this project lays the groundwork for a system that ensures advanced technologies serve humanity's best interests through transparent, ethical, and accountable deployment. The requirements and prototypes developed during this phase will serve as the blueprint for future implementation, ensuring that the final system precisely addresses the needs identified through our collaborative process with stakeholders.

通过彻底记录需求并创建有效的原型以实现 AI 审计流程的现代化,该项目为确保先进技术通过透明、合乎道德和负责任的部署服务于人类最大利益的系统奠定了基础。在此阶段开发的需求和原型将作为未来实施的蓝图,确保最终系统精确满足通过与利益相关者的协作过程确定的需求。

Changelog

MIT Licensed