Audits are critical mechanisms for identifying the risks and limitations of deployed artificial intelligence (AI) systems. However, the effective execution of AI audits remains incredibly difficult. As a result, practitioners make use of various tools to support their efforts. Drawing on interviews with 35 AI audit practitioners and a landscape analysis of 390 tools, we map the current ecosystem of available AI audit tools. While there are many tools designed to assist practitioners with setting standards and evaluating AI systems, these tools often fell short of supporting the accountability goals of AI auditing in practice. We thus highlight areas for future tool development beyond evaluation -- from harms discovery to advocacy -- and outline challenges practitioners faced in their efforts to use AI audit tools. We conclude that resources are lacking to adequately support the full scope of needs for many AI audit practitioners and recommend that the field move beyond tools for just evaluation, towards more comprehensive infrastructure for AI accountability.

翻译：审计是识别已部署人工智能系统风险和局限性的关键机制。然而，有效执行AI审计仍然极为困难。为此，从业者利用各种工具来支持其工作。基于对35位AI审计从业者的访谈及对390款工具的全景分析，我们绘制了当前可用AI审计工具生态图谱。尽管存在许多旨在协助从业者制定标准并评估AI系统的工具，但这些工具在实践中往往难以支持AI审计的问责目标。因此，我们指出了未来工具开发需超越评估范畴的重点领域——从危害发现到倡导倡议——并概述了从业者在使用AI审计工具时面临的挑战。我们认为现有资源尚不足以充分满足众多AI审计从业者的全方位需求，建议该领域应突破仅聚焦评估的工具思维，转向构建更完善的AI问责基础设施。