Research on artificial intelligence and democracy has grown quickly over the last decade. A shared conclusion in this literature is that AI does not create new democratic problems so much as it makes old ones worse. We now see this across information ecosystems, in elections, and in public administration. However, despite growing evidence, we lack a clear way to prioritize risks in this area, compare them across domains, and identify where democratic control is most likely to break down. So, our problem is: How can we systematize the problems that AI systems pose to democratic processes? This paper argues that principal agent theory may fit the task. In many phases of democratic systems, principals delegate key functions to AI systems and their providers without really being able to monitor how these systems operate or the outputs they produce. Treating AI as a delegation problem helps identify accountability gaps and other governance failures. Most importantly, as we shall illustrate, it provides metrics for empirical assessments of AI impact on democracy. As a second analytical element, we draw on the NIST AI Risk Management Framework and its seven characteristics of trustworthy AI, which supply substantive criteria for evaluating delegated tasks. Operationalized across the three domains through measurable indicators and domain specific trustworthiness criteria, we propose an analytical framework that centers on institutional assessability as the central condition for democratic control over AI. However, we stress that how severe a harm is, and how much risk is acceptable, are evaluative judgments that current methodologies neither acknowledge nor operationalize. This becomes acute when such evaluative judgments are (silently) delegated to private vendors. We identify this as a strong limitation left for future work.

翻译：过去十年间，关于人工智能与民主的研究迅速增长。该领域的文献一致认为，人工智能并未催生新的民主问题，而是加剧了原有的民主困境。这种影响如今已显现于信息生态系统、选举进程及公共行政领域。然而，尽管证据日益增多，我们仍缺乏明确的方法来优先评估该领域的风险、跨领域比较风险，以及识别民主控制最可能失效的环节。因此，我们的核心问题在于：如何系统化人工智能对民主进程造成的挑战？本文提出，委托-代理理论或可承担此任。在民主系统的多个环节中，委托人将关键职能委托给人工智能系统及其供应商，却无法真正监督这些系统的运行方式或产出结果。将人工智能视为委托问题，有助于识别问责缺口及其他治理失灵。最为重要的是，正如我们将要阐明的，这一视角为实证评估人工智能对民主的影响提供了量化指标。作为第二项分析要素，我们借鉴了美国国家标准与技术研究院人工智能风险管理框架及其可信赖人工智能的七项特征——这些特征为评估委托任务提供了实质性标准。通过可量化指标及领域特定可信度标准，我们在三个领域中进行可操作化研究，提出以机构可评估性作为民主控制人工智能核心条件的分析框架。但需强调的是，危害的严重程度与可接受的风险阈值，本质上属于价值判断——现有方法论既未承认也未量化此类判断。当这些价值判断被（无声地）委托给私营供应商时，问题尤为尖锐。我们将其作为未来研究亟需突破的重大局限加以指明。