IAPP AI Governance Practice Test 2025 - Free AI Governance Practice Questions and Study Guide

A consequence of poorly implemented interventions in AI governance is indeed resentment from stakeholders. When governance measures are not effectively executed, stakeholders—including employees, customers, and business partners—may feel marginalized, ignored, or negatively impacted by the decisions made. This can lead to a breakdown of trust and collaboration, as stakeholders may perceive that their concerns and needs were not adequately considered during the implementation of AI governance policies.

For example, if an organization introduces AI systems without sufficient transparency or involvement from those affected, stakeholders might react negatively, feeling that their inputs were disregarded or that the change threatens their interests or security. This resentment can have long-term effects, not just on individual satisfaction, but also on the overall organizational culture and effectiveness, as stakeholders may become less engaged or resistant to further governance measures.

In contrast, the other options suggest positive outcomes that would not typically result from poor implementation. Increased stakeholder satisfaction and enhanced effectiveness of all interventions imply that the governance measures are being well-received and operated efficiently, which contradicts the premise of poor implementation. Similarly, greater compliance with existing regulations would signify that governance efforts are successfully aligning with legal requirements and best practices, also misaligned with the consequences of poor implementation.