Quantifying and Mitigating Dataset Biases in Video Understanding Tasks across Cultural Contexts

Abstract

Cross-cultural biases embedded in video datasets pose significant challenges to the fairness and generalization of video understanding models. Existing benchmarks are predominantly constructed from Western-centric visual corpora, leading to performance degradation when models are applied to underrepresented cultural contexts. This paper presents a comprehensive framework for quantifying and mitigating cultural biases in video understanding tasks. A multi-level analysis is conducted to identify cultural skew in existing datasets, revealing disparities in representation, annotation practices, and modality alignment. To address these biases, we propose a set of mitigation strategies encompassing culturally adaptive data augmentation, architecture-aware modality calibration, and causal intervention-based debiasing. Extensive experiments on action recognition, sign language translation, and captioning tasks demonstrate significant improvements in cultural fairness and semantic alignment. Evaluation metrics, including the Cultural Relevance Index (CRI), Fairness Gap (FG), and Modality Gap Index (MGI), provide quantitative evidence of improved cross-cultural robustness. Ethical considerations surrounding annotation, deployment, and interpretability are also discussed. This work contributes toward equitable and culturally inclusive video understanding systems that generalize beyond monocultural datasets.