Rising concern for the societal implications of artificial intelligence systems has inspired a wave of academic and journalistic literature in which deployed systems are audited for harm by investigators from outside the organizations deploying the algorithms. However, it remains challenging for practitioners to identify the harmful repercussions of their own systems prior to deployment, and, once deployed, emergent issues can become difficult or impossible to trace back to their source. In this paper, we introduce a framework for algorithmic auditing that supports artificial intelligence system development end-to-end, to be applied throughout the internal organization development lifecycle. Each stage of the audit yields a set of documents that together form an overall audit report, drawing on an organization's values or principles to assess the fit of decisions made throughout the process. The proposed auditing framework is intended to contribute to closing the accountability gap in the development and deployment of large-scale artificial intelligence systems by embedding a robust process to ensure audit integrity.

中文翻译：

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缩小 AI 问责制差距：为内部算法审计定义端到端框架

对人工智能系统的社会影响的日益关注激发了学术和新闻文献的浪潮，其中部署的系统由部署算法的组织外部的调查人员审计其危害。然而，从业者在部署之前识别他们自己系统的有害影响仍然具有挑战性，并且一旦部署，紧急问题可能变得难以或不可能追溯到其源头。在本文中，我们介绍了一个算法审计框架，该框架支持端到端的人工智能系统开发，应用于整个内部组织开发生命周期。审计的每个阶段都会产生一组文件，这些文件共同构成了一份总体审计报告，借鉴了组织的 的价值观或原则来评估在整个过程中做出的决定的适合性。拟议的审计框架旨在通过嵌入一个强大的流程来确保审计完整性，从而有助于缩小大规模人工智能系统开发和部署中的问责制差距。