Artificial intelligence (AI) is increasingly intervening in our lives, raising widespread concern about its unintended and undeclared side effects. These developments have brought attention to the problem of AI auditing: the systematic evaluation and analysis of an AI system, its development, and its behavior relative to a set of predetermined criteria. Auditing can take many forms, including pre-deployment risk assessments, ongoing monitoring, and compliance testing. It plays a critical role in providing assurances to various AI stakeholders, from developers to end users. Audits may, for instance, be used to verify that an algorithm complies with the law, is consistent with industry standards, and meets the developer's claimed specifications. However, there are many operational challenges to AI auditing that complicate its implementation.
In this work, we examine a key operational issue in AI auditing: what type of access to an AI system is needed to perform a meaningful audit? Addressing this question has direct policy relevance, as it can inform AI audit guidelines and requirements. We begin by discussing the factors that auditors balance when determining the appropriate type of access, and unpack the benefits and drawbacks of four types of access. We conclude that, at minimum, black-box access -- providing query access to a model without exposing its internal implementation -- should be granted to auditors, as it balances concerns related to trade secrets, data privacy, audit standardization, and audit efficiency. We then suggest a framework for determining how much further access (in addition to black-box access) to grant auditors. We show that auditing can be cast as a natural hypothesis test, draw parallels hypothesis testing and legal procedure, and argue that this framing provides clear and interpretable guidance on audit implementation.
Towards AI Transparency and Accountability: A Global Framework for Exchanging Information on AI Systems
University College Dublin（都柏林大学）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Duke University（杜克大学）；Trinity College Dublin, Technological University Dublin（都柏林圣三一学院、技术大学都柏林）
Enhancing transparency in AI-powered customer engagement
Pegasystems（Pegasystems公司）；Oracle（甲骨文公司）；EMC（易安信公司）；University of California at Berkeley（加州大学伯克利分校）；University of Massachusetts at Amherst（马萨诸塞大学阿默斯特分校）
"Taking Stock at FAccT": Using Participatory Design to Co-Create a Vision for the Fairness, Accountability and Transparency Community
Northeastern University Boston United States；Munich Center for Machine Learning (MCML) Munich Germany；University of Mannheim Mannheim Germany；StickFlux Labs \& University of Chicago United States；Northeastern University；Munich Center for Machine Learning (MCML)；University of Mannheim；StickFlux Labs \& University of Chicago
Trust and Transparency in AI: Industry Voices on Data, Ethics, and Compliance
ADAPT Research Centre, School of Computer Science, University of Galway（高威大学计算机科学学院 ADAPT 研究中心）；University of Milan（米兰大学）；ADAPT Research Centre, Trinity College Dublin（都柏林三一学院 ADAPT 研究中心）
Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction
University of Gondar（贡德尔大学）；College of Engineering and Applied Science, University of Colorado, Colorado Springs（科罗拉多大学科罗拉多斯普林斯分校工程与应用科学学院）；Can Tho University（芹苴大学）；Ewha Womans University（梨花女子大学）；University of Colorado, Colorado Springs（科罗拉多大学科罗拉多斯普林斯分校）
Bureaucratic Silences: What the Canadian AI Register Reveals, Omits, and Obscures
Department of Computer Science University of Toronto Toronto Ontario Canada（计算机科学系多伦多大学多伦多安大略加拿大）；Faculty of Information University of Toronto Toronto Ontario Canada（信息学院多伦多大学多伦多安大略加拿大）；University of Toronto（多伦多大学）
