A new research paper proposes a framework to detect and measure the dangers artificial intelligence poses to democratic processes, addressing a critical gap in AI governance. The paper, authored by Sandri and Novelli (2026), argues that AI does not create new democratic problems so much as it makes old ones worse, across information ecosystems, elections, and public administration.
The Principal-Agent Problem in AI Governance
According to the paper, many phases of democratic systems involve principals delegating key functions to AI systems and their providers without sufficient ability 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. The authors state that this approach provides metrics for empirical assessments of AI impact on democracy.
Applying the NIST AI Risk Management Framework
The paper draws on the NIST AI Risk Management Framework and its seven characteristics of trustworthy AI, which supply substantive criteria for evaluating delegated tasks. Operationalized across three domains through measurable indicators and domain-specific trustworthiness criteria, the framework centers on institutional assessability as the central condition for democratic control over AI.
Toward Institutional Assessability
Institutional assessability refers to the ability of democratic institutions to assess AI systems effectively. The framework aims to systematize the problems AI poses to democratic processes, prioritize risks, compare them across domains, and identify where democratic control is most likely to break down.
Limitations and Future Work
The authors 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. They identify this as a strong limitation left for future work.
Implications for Enterprise Technology Leaders
For enterprise technology leaders overseeing AI deployment, the framework offers a structured approach to assess accountability and trustworthiness. Understanding principal-agent dynamics and the NIST framework can help ensure AI systems used in public administration, elections, and information ecosystems are transparent and controllable. The paper's emphasis on institutional assessability underscores the need for robust monitoring and governance mechanisms in AI deployments that affect democratic processes.
