We introduce the psychometric concepts of bias and fairness in a multimodal machine learning context assessing individuals’ hireability from prerecorded video interviews. We collected interviews from 733 participants and hireability ratings from a panel of trained annotators in a simulated hiring study, and then trained interpretable machine learning models on verbal, paraverbal, and visual features extracted from the videos to investigate unimodal versus multimodal bias and fairness. Our results demonstrate that, in the absence of any bias mitigation strategy, combining multiple modalities only marginally improves prediction accuracy at the cost of increasing bias and reducing fairness compared to the least biased and most fair unimodal predictor set (verbal). We further show that gender-norming predictors only reduces gender predictability for paraverbal and visual modalities, while removing gender-biased features can achieve gender blindness, minimal bias, and fairness (for all modalities except for visual) at the cost of some prediction accuracy. Overall, the reduced-feature approach using predictors from all modalities achieved the best balance between accuracy, bias, and fairness, with the verbal modality alone performing almost as well. Our analysis highlights how optimizing model prediction accuracy in isolation and in a multimodal context may cause bias, disparate impact, and potential social harm, while a more holistic optimization approach based on accuracy, bias, and fairness can avoid these pitfalls.
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