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arXiv 2001.00973cs.CY
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缩小AI责任差距：定义内部审计的端到端框架

Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing

Inioluwa Deborah Raji, Andrew Smart, Rebecca N. White, Margaret Mitchell, Timnit Gebru, Ben Hutchinson, Jamila Smith-Loud, Daniel Theron, Parker Barnes

备注：
Accepted to ACM FAT* (Fariness, Accountability and Transparency) conference 2020. Full workable templates for the documents of the SMACTR framework presented in the paper can be found here https://drive.google.com/drive/folders/1GWlq8qGZXb2lNHxWBuo2wl-rlHsjNPM0?usp=sharing
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摘要：

对人工智能系统的社会影响的日益关注激发了一波学术和新闻文献，其中部署的系统由部署算法的组织外部的调查人员进行审计。然而，对于从业人员来说，在部署之前确定自己的系统的有害影响仍然具有挑战性，并且一旦部署，紧急问题可能难以或不可能追溯到其根源。在本文中，我们介绍了一个框架，算法审计，支持人工智能系统开发端到端，在整个内部组织的开发生命周期。审计的每个阶段产生一套文件，这些文件共同构成一份总体审计报告，利用组织的价值观或原则来评估整个过程中所做决策的适用性。拟议的审计框架旨在通过嵌入一个确保审计完整性的强有力程序，帮助缩小在开发和部署大规模人工智能系统方面的问责差距。

英文摘要：

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.

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。

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