MAG-BERT-ARL for Fair Automated Video Interview Assessment

Abstract

Potential biases within automated video interview assessment algorithms may disadvantage specific demographics due to the collection of sensitive attributes, which are regulated by the General Data Protection Regulation (GDPR). To mitigate these fairness concerns, this research introduces MAG-BERT-ARL, an automated video interview assessment system that eliminates reliance on sensitive attributes. MAG-BERT-ARL integrates Multimodal Adaptation Gate and Bidirectional Encoder Representations from Transformers (MAG-BERT) model with the Adversarially Reweighted Learning (ARL). This integration aims to improve the performance of underrepresented groups by promoting Rawlsian Max-Min Fairness. Through experiments on the Educational Testing Service (ETS) and First Impressions (FI) datasets, the proposed method demonstrates its effectiveness in optimizing model performance (increasing Pearson correlation coefficient up to 0.17 in the FI dataset and precision up to 0.39 in the ETS dataset) and fairness (reducing equal accuracy up to 0.11 in the ETS dataset). The findings underscore the significance of integrating fairness-enhancing techniques like ARL and highlight the impact of incorporating nonverbal cues on hiring decisions.

Keywords

• Automatic video interview assessment
• fairness
• model interpretability
• adversarial learning