Debiased Multimodal Personality Understanding throu...

Debiased Multimodal Personality Understanding throu... | AI Research

Key Takeaways

Debiased Multimodal Personality Understanding through Dual Causal Intervention Multimodal personality understanding aims to infer traits like the Big Five or...
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.

Paper AbstractExpand

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 this https URL

Debiased Multimodal Personality Understanding through Dual Causal Intervention
Multimodal personality understanding aims to infer traits like the Big Five or MBTI from video, audio, and text. While modern AI models can achieve high accuracy by analyzing these signals, they often rely on "spurious correlations"—shortcuts where the model associates specific demographic traits (like age, gender, or race) with certain personality types. This leads to unfair and biased predictions. This paper introduces a new framework called the Dual Causal Adjustment Network (DCAN) to identify and remove these biases, ensuring that personality assessments are based on genuine behavioral cues rather than demographic stereotypes.

Identifying the Bias

The authors use a Structural Causal Model (SCM) to map how bias enters the system. They categorize these biases into two types: observable confounders, such as demographic attributes, and unobservable confounders, such as a person's transient emotional state or social background. Because these factors influence both the input data and the final personality prediction, the model learns to rely on them as "shortcuts." To fix this, the researchers move beyond traditional predictive modeling to causal intervention, which allows the AI to "block" the influence of these unfair associations.

The Dual Causal Adjustment Network

The DCAN framework uses two distinct modules to clean the data:

Back-door Adjustment Causal Learning (BACL): This module targets observable demographic biases. It uses a "confounder dictionary" to store prototypes of demographic groups. By comparing input features against these prototypes, the model can isolate and remove the influence of demographic factors from the visual, audio, and textual data.
Front-door Adjustment Causal Learning (FACL): This module addresses latent, unobservable biases. It uses a learned mediator dictionary to intervene on the internal representations of the data, helping the model ignore hidden factors like temporary mood fluctuations that might otherwise skew the results.

Performance and Fairness

To test their approach, the researchers introduced the Demographic-annotated Multimodal Student Personality (DMSP) dataset, which provides the necessary labels to measure fairness in MBTI-based personality assessment. When tested against existing benchmarks like CFI-V2 and the new DMSP dataset, the DCAN framework consistently outperformed state-of-the-art models. Beyond just improving prediction accuracy, the model significantly boosted fairness metrics, showing substantial improvements in equal opportunity and demographic parity.

Why This Matters

This research highlights a critical challenge in human-centered AI: ensuring that automated personality analysis is objective and equitable. By demonstrating that causal intervention can effectively disentangle genuine behavioral signals from demographic noise, the authors provide a pathway for building more reliable and ethical AI systems. The code and the new DMSP dataset are publicly available to encourage further research into fairness and bias mitigation in multimodal learning.