通过双重因果干预，消除多模态人格理解中的偏见。
Debiased Multimodal Personality Understanding through Dual Causal Intervention
- 构建因果模型，用原型字典阻断显性偏见，用中介字典处理隐性偏见。
- 在CFI-V2和自建数据集上准确率分别达92.11%和92.90%，公平性提升超15%。
- 适合关注公平性、可解释性的人格分析研究者使用。
多模态人格理解在以人为本的人工智能中至关重要。以往方法主要学习视频中丰富的多模态表征来理解人格，但常受主体偏见（如可见年龄与不可见心理状态）影响，因被试来自不同人口背景。学习这些虚假关联可能导致不公平的人格判断。本文从因果视角分析此类偏见，提出双因果调节网络（DCAN），包含后门调整模块（BACL）通过原型式混杂因子字典阻断显性人口特征的虚假关联，以及前门调整模块（FACL）通过学习中介字典干预，缓解潜在不可观测偏见，实现表征的因果解耦。我们构建了带人口标注的多模态学生人格数据集（DMSP），以支持公平性分析。在基准数据集CFI-V2和自建数据集上，DCAN均显著提升准确率至92.11%和92.90%，平等机会与人口均等性指标分别提升6.57%/7.97%（CFI-V2）和15.38%/20.06%（DMSP）。代码与数据集已开源。
原文摘要 · Abstract (English)
Multimodalpersonalityunderstandingplaysacriticalroleinhuman centered artificial intelligence. Previous work mainly focus on learn-ing rich multimodal representations for video personality under standing. However, they often suffer from potential harm caused by subject bias (e.g., observable age and unobservable mental states), as subjects originate from diverse demographic backgrounds. Learn ing such spurious associations between multimodal features and traits may lead to unfair personality understanding. In this work, weconstruct aStructural Causal Model (SCM)toanalyze theimpact of these biases from a causal perspective, and propose a novel Dual Causal Adjustment Network (DCAN) to mitigate the interference of subject attributes on personality understanding. Specifically, we design a Back-door Adjustment Causal Learning (BACL) module to block spurious correlations from observable demographic factors via a prototype-based confounder dictionary, and subsequently ap ply a Front-door Adjustment Causal Learning (FACL) module to ad dress latent and unobservable biases throughalearnedmediatordic tionary intervention, thereby achieving causal disentanglement of representations for deconfounded reasoning. Importantly, we con struct a Demographic-annotated Multimodal Student Personality (DMSP) dataset to support the analysis and discussion of fairness related factors. Extensive experiments on the benchmark dataset CFI-V2 and our DMSPdataset demonstrate that DCAN consistently improves prediction accuracy, reaching 92.11% and 92.90%, respec tively. Meanwhile, the improvementsinthefairnessmetricsofequal opportunity and demographic parity are 6.57% and 7.97% on CFI-V2, and 15.38% and 20.06% on the DMSP dataset. Our code and DMSP dataset are available at https://github.com/Sabrina-han/DCAN
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