This paper proposes VIBE, an annotation-free method that selects video summaries for human decision-making by scoring task relevance and visual grounding without retraining.

We show how perceived post-selection bias distorts strategic effort in merit-based selection, leading to disparities. Our model quantifies interventions to reduce inequity by adjusting selectivity and perceived valuation gaps.