형사정책시스템 알고리즘 구축 과정에서의 편향성

Abstract

A discussion is urgently needed to identify the efficiency and effectiveness of the criminal justice algorithm, which is intended to develop and operate in an automated decision-making form and to reduce the bias that is understood as a negative effect of the algorithm. In the method of Active Crime Prediction, which comprehensively analyzes current data and accessible public data and transmits the predicted and analyzed information to the on-site police in real-time, it utilizes pure social data rather than on-site information or front-line information. It is defined as a predictive police activity that determines the target of crime prevention or police intervention through statistical prediction and is facing a change to the establishment of criminal justice procedures through an algorithmic system as a type. Overseas, systems such as DAS, PredPol, and COMPAS are being produced and operated by private companies. However, in Korea, it is only at the stage of technical discussion and testing of the evolved method so far, and there are many issues and issues regarding the elements and algorithms that must be equipped legally and institutionally. Nevertheless, the realm of these debates and issues is obscured by technological advances. Therefore, it is important to exclude technical aspects and identify the problems and issues through the examples of the United States and the United Kingdom, which are leading countries in introducing criminal justice algorithm systems in the humanities and social sciences. In this study, the problems that appeared through previous studies and cases were classified and explored according to the system operation stage (construction, utilization, management). The various issues and problems analyzed are ultimately problems that appeared in the use of the algorithm. It also had a structure that reduced it to a problem created by the person using the technology. It is interpreted as a limitation in that it cannot provide a clear solution to the ongoing debate or reflect it in the field. Therefore, in the field of criminal policy dealing with predictive policing and recidivism prediction, we examine the bias accompanying the automated judgment of algorithms, and envision the prerequisite for detailed complementation and construction in terms of normative aspects and data collection and utilization to solve problems.