AI proctoring tools excel at detecting patterns but cannot account for individual traits or environmental conditions that shape test-taker behavior. Without careful human review, AI-generated flags risk being misinterpreted, leading to unfair outcomes. This session explores common human biases—confirmation bias, automation bias, and the black box problem—and their role in shaping how AI data is used. Attendees will learn practical strategies to critically evaluate AI outputs, implement transparent review protocols, and uphold fairness and trust in online assessments. Join us to explore how thoughtful human oversight can ensure that technology supports, rather than undermines, student equity.