Faculty members at a university are concerned about potential inherent bias and inconsistency in one...

Basic Concept: Responsible AI principles each address different aspects of trustworthy AI behavior. When stakeholders are concerned about both bias and inconsistency — specifically that the same or equivalent work might receive different treatment from the AI system — the principle of consistency is most directly relevant. CompTIA SecAI+ covers responsible AI principles under governance.

Why C is Correct: Consistency in AI systems means the model applies the same rules, standards, and decision criteria uniformly across all inputs and user groups without variation based on characteristics unrelated to the task. An AI plagiarism detection system that produces inconsistent results across different student submissions or demographic groups fails the consistency principle, which directly addresses both the bias concern (differential treatment) and inconsistency concern the faculty have raised.

Why A is Wrong: Transparency relates to openness about how the AI system works and what data it uses. While valuable for understanding the system, transparency alone does not ensure that the system applies its rules uniformly or consistently.

Why B is Wrong: Explainability means the system can articulate why it made a particular decision. While useful for understanding individual cases, it does not guarantee that decisions are made with equal consistency across different submissions or groups.

Why D is Wrong: Accountability identifies who is responsible for AI system decisions and outcomes. It is a governance principle about ownership and responsibility rather than about ensuring uniform application of evaluation criteria.