형사사법절차 전자화와 빅 데이터를 이용한 양형합리화 방안 모색

Abstract

We are in a new era of big data. “Big data” is an inclusive term used to describe vast amounts of information. Contrary to traditional data which is typically stored in a relational database, big data varies in terms of volume, velocity, and variety. Big data is typically generated in large volumes. Big data comes from everywhere: social data, machine data and transactional data. Analytics has always involved attempts to improve decision making, and big data contributes that. Large organizations are seeking both faster and better decisions with big data, and Law enforcement also are seeking them. The big data trend promises that harnessing the wealth and volume of information leads to better operational efficiency, and competitive advantage. Traditional analytics tools are not well suited to capturing the full value of big data. Machine learning is ideal for unlocking hidden profits in big data. It fulfills the promise of extracting value from big data with less reliance on human intervention. It is well suited to the complexity of dealing with disparate data sources and the huge variety of variables and amounts of data involved. And unlike traditional analysis, machine learning thrives on growing datasets. The more data fed into a machine learning system, the more it can learn andapply the results to higher quality insights. One of the basic arguments in this article is extended enforcement of the digitalization of the criminal justice procedure. With the execution of Act on promoting the digitalization of the criminal justice procedure and Act on the use of electronic documents in summary procedures, A criminal justice agency shall store and keep decisions, indictments, warrants, records and other documents concerning criminal justice affairs by using their systems. The stored and kept data in this procedure is intrinsically characterized as Big data in terms of volume and variety. Byproducts of all this data are the new insights that can drive judicial decision making in new way. The new insights will contribute to a process of reform of our justice systems to render them more effective. In korea, substantial disparity in criminal sentencing is a critical issue. The Sentencing Guidelines were designed to eliminate this sentencing disparities. This article suggests that big data analytics will help to improve eliminating sentencing disparity and effective law-enforcement. This article presents the actuarial risk assessment instrument used to predict the risk of future dangerousness in United States. Actuarial method of risk assessment is designed to predict specific behaviors within a specific time frame. The stated goal of the actuarial method is to predict future dangerousness in a relative sense, by comparing an individual to a norm-based reference group. Big data analytics will also improve this actuarial risk assessment. This article also suggests Machine learning-based legal expert system that help judges explain why they decided that way. Since machine learning algorithm can be very good at detecting hard to observe relationship between data, it may be possible to detect obscured association between certain variables in criminal case and particular legal outcomes. Probably this expert system outperforms several experts in terms of predicting judicial outcomes.