用可控视频检测动作识别模型对肤色的偏见
Identifying Ethical Biases in Action Recognition Models
- 通过合成视频控制肤色等身份特征，保持动作一致
- 发现多个主流模型在相同动作下对不同肤色有显著识别偏差
- 为公平性审计提供可复现框架，适合监管与模型开发者
人类动作识别（HAR）模型正被广泛应用于高风险场景，但其在不同人种外观下的公平性尚未得到系统分析。本文提出一种基于合成视频数据的审计框架，利用全控制视觉身份属性（如肤色）的生成技术，突破了以往仅关注静态图像或姿态估计的局限。通过在BEDLAM仿真平台上进行受控干预，我们验证了即使运动模式完全相同，部分主流HAR模型仍对不同肤色表现出统计显著的识别偏差。结果揭示模型可能隐含非期望的视觉关联，且存在群体间系统性错误。本工作贡献了一个可复现的审计框架，助力构建更透明、可问责的HAR系统，以应对未来监管要求。
原文摘要 · Abstract (English)
Human Action Recognition (HAR) models are increasingly deployed in high-stakes environments, yet their fairness across different human appearances has not been analyzed. We introduce a framework for auditing bias in HAR models using synthetic video data, generated with full control over visual identity attributes such as skin color. Unlike prior work that focuses on static images or pose estimation, our approach preserves temporal consistency, allowing us to isolate and test how changes to a single attribute affect model predictions. Through controlled interventions using the BEDLAM simulation platform, we show whether some popular HAR models exhibit statistically significant biases on the skin color even when the motion remains identical. Our results highlight how models may encode unwanted visual associations, and we provide evidence of systematic errors across groups. This work contributes a framework for auditing HAR models and supports the development of more transparent, accountable systems in light of upcoming regulatory standards.
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