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Multidimensional Subset-based Systems for Bias Elimination Within Binary Classification Datasets
KyeongSu Byun, Goo Kim, Joonho Kwon
http://doi.org/10.5626/JOK.2023.50.5.383
As artificial intelligence technology develops, artificial intelligence-related fairness issues are drawing attention. As a result, many related studies have been conducted on this issue, but most of the research has focused on developing models and training methods. Research on removing bias existing in data used for learning, which is a fundamental cause, is still insufficient. Therefore, in this paper, we designed and implemented a system that divides the biases existing within the data into label biases and subgroup biases and removes the biases to generate datasets with improved fairness. The proposed system consists of two steps: (1) subset generation and (2) bias removal. First, the subset generator divides the existing data into subsets on formed by a combination of values in an datasets. Subsequently, the subset is divided into dominant and weak groups based on the fairness indicator values obtained by validating the existing datasets based on the validation datasets. Next, the bias remover reduces the bias shown in the subset by repeating the process of sequentially extracting and verifying the dominant group of each subset to reduce the difference from the weak group. Afterwards, the biased subsets are merged and a fair data set is returned. The fairness indicators used for the verification use the F1 score and the equalized odd. Comprehensive experiments with real-world Census incoming data, COMPAS data, and bank marketing data as verification data demonstrated that our proposed system outperformed the existing technique by yielding a better fairness improvement rate and providing more accuracy in most machine learning algorithms.
A Study of Metric and Framework Improving Fairness-utility Trade-off in Link Prediction
Heeyoon Yang, YongHoon Kang, Gahyung Kim, Jiyoung Lim, SuHyun Yoon, Ho Seung Kim, Jee-Hyong Lee
http://doi.org/10.5626/JOK.2023.50.2.179
The advance in artificial intelligence (AI) technology has shown remarkable improvements over the last decade. However, sometimes, AI makes biased predictions based on real-world big data that intrinsically contain discriminative social factors. This problem often arises in friend recommendations in Social Network Services (SNS). In the case of social network datasets, Graph Neural Network (GNN) is utilized for training these datasets, but it has a high tendency to connect similar nodes (Homophily effect). Furthermore, it is more likely to make a biased prediction based on socially sensitive attributes, such as, gender or religion, making it ethically more problematic. To overcome these problems, various fairness-aware AI models and fairness metrics have been proposed. However, most of the studies used different metrics to evaluate fairness and did not consider the trade-off relationship that existed between accuracy and fairness. Thus, we propose a novel fairness metric called Fairβ-metri which takes both accuracy and prediction into consideration, and a framework called FairU that shows outstanding performance in the proposed metric.
Fair Feature Distillation Using Teacher Models of Larger Architecture
http://doi.org/10.5626/JOK.2021.48.11.1176
Achieving algorithmic fairness is becoming increasingly essential for various vision applications. Although a state-of-the-art fairness method, dubbed as MMD-based Fair feature Distillation (MFD), significantly improved accuracy and fairness via feature distillation based on Maximum Mean Discrepancy (MMD) compared to previous works, MFD could be limitedly applied into when a teacher model has the same architecture as a student model. In this paper, based on MFD, we propose a systematic approach that mitigates unfair biases via feature distillation of a teacher model of larger architecture, dubbed as MMD-based Fair feature Distillation with a regressor (MFD-R). Throughout the extensive experiments, we showed that our MFD-R benefits from the use of the larger teacher compared to MFD as well as other baseline methods.
A Network-Aware Congestion Control Scheme for Improving the Performance of C-TCP over HBDP Networks
Junyeol Oh, Dooyeol Yun, Kwangsue Chung
While today’s networks have been shown to exhibit HBDP (High Bandwidth Delay Product) characteristics, the legacy TCP increases the size of the congestion window slowly and decreases the size of the congestion window drastically such that it is not suitable for HBDP Networks. In order to solve this problem with the legacy TCP, many congestion control TCP mechanisms have been proposed. C-TCP (Compound-TCP) is a hybrid TCP which is a synergy of delay-based and loss-based approaches. C-TCP adapts the decreasing rate of the delay window without considering the congestion level, leading to degradation of performance. In this paper, we propose a new scheme to improve the performance of C-TCP. By controlling the increasing and decreasing rates according to the congestion level of the network, our proposed scheme can improve the bandwidth occupancy and fairness of C-TCP. Through performance evaluation, we show that our proposed scheme offers better performance in HBDP networks as compared to the legacy C-TCP.
Network Adaptive Congestion Control Scheme to Improve Bandwidth Occupancy and RTT Fairness in HBDP Networks
These days, the networks have exhibited HBDP (High Bandwidth Delay Product) characteristics. The legacy TCP slowly increases the size of the congestion window and drastically decreases the size of a congestion window. The legacy TCP has been found to be unsuitable for HBDP networks. TCP mechanisms for solving the problems of the legacy TCP can be categorized into the loss-based TCP and the delay-based TCP. Most of the TCP mechanisms use the standard slow start phase, which leads to a heavy packet loss event caused by the overshoot. Also, in the case of congestion avoidance, the loss-based TCP has shown problems of wastage in terms of the bandwidth and RTT (Round Trip Time) fairness. The delay-based TCP has shown a slow increase in speed and low occupancy of the bandwidth. In this paper, we propose a new scheme for improving the over shoot, increasing the speed of the bandwidth and overcoming the bandwidth occupancy and RTT fairness issues. By monitoring the buffer condition in the bottleneck link, the proposed scheme does congestion control and solves problems of slow start and congestion avoidance. By evaluating performance, we prove that our proposed scheme offers better performance in HBDP networks compared to the previous TCP mechanisms.
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