Fair Clustering with Fair Correspondence Distribution

Lee, Woojin; Ko, Hyungjin; Byun, Junyoung; Yoon, Tae‐Ho; Lee, Jae‐Wook

doi:10.1016/j.ins.2021.09.010

Cited by 8 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Post-processing mechanisms seek to improve fairness by modifying the output scores of the classifier. For example, it computes the threshold value where the privileged group and unprivileged group are both fairly classified [30]- [32].…”

Section: Mechanisms For Improving Fairnessmentioning

confidence: 99%

Fairness-Aware Multimodal Learning in Automatic Video Interview Assessment

Kim,

Choi,

Yoon

et al. 2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

With the ever-growing reliance on Artificial Intelligence (AI) across diverse domains, there is an increasing concern surrounding the possibility of biases and unfairness inherent in AI systems. Fairness problems in automatic interview assessment systems, especially video-based automated interview assessments, have less been addressed despite their prevalence in the recruiting field. In this paper, we propose a method that resolves fairness problems in an automated interview assessment system that uses multimodal data as input. This is mainly done by minimizing the Wasserstein distance between two sensitive groups by introducing a regularization term. Subsequently, we employ a hyperparameter that can control the trade-off between fairness and accuracy. To test our method in various data settings, we suggest a preprocessing method that can manually adjust the underlying degree of unfairness in the training data. Experimental results show that our method presents state-of-the-art results in terms of fairness compared to previous methods.

show abstract

Section: Mechanisms For Improving Fairnessmentioning

confidence: 99%