Fairness Improvement Technology and Visualization Services for binary classification datasets collected on a batch basis

Abstract

Since securing data is essential in the field of artificial intelligence, the importance of data collection and purification is steadily increasing. On the other hand, there are typical problems arising in the process of collecting data, such as the black bias problem in COMPAS and the fairness problem that exists within the data, such as the facial recognition bias problem. Accordingly, by designing and implementing a system that corrects fairness by removing bias existing in the dataset itself, we tried to help research on artificial intelligence models. The 'Fairness Improvement Technology and Visualization Services for binary classification datasets collected on a batch basis' consists of a subset generator that separates the initial binary classification datasets with unique values, a bias remover that removes bias by comparing and verifying each subset, and a visualization module that visualizes the corrected data as a web service. In addition, the proposed system in this paper presents a data-level research method that eliminates bias in the data itself without modifying additional learning or algorithms, and confirms the potential of the proposed system using COMPAS Data[1], Adult Census Income Data[2] as validation data.