빅데이터 형사정책의 현안과 과제

Criminal Law and Policy on the Issues of Big Data

This essay studies those issues of criminal law and policy on big data-based criminal legislation and crime prevention programs, including data privacy protection and predictive policing. Big data technology provides new kinds of future prospective based on the analysis of various data, and thus supports policy decision and measures against social problems, including crimes. Big data-based policy, however, has its own risk or limitation when data or algorithm used does not secure its own credibility. It may results no legitimacy or effectiveness, especially in criminal justice policies. One of the critical issues on big data in criminal justice is how to make balance between privacy protection and promoting big data-based policy. How can we secure such balance in criminal legislation and policy-making? Most of all, considering that the personal subject of data should be both provider and user in the context of big data, the person should be a right-holder as well as an object of protection. Legal protection for such double-faced status should be measured. Another issue related to big data in criminal justice is predictive policing programs, especially have been put into practice by police forces in the US. It is expected that such program will contribute more effective crime prevention and efficient intervention of criminal justice resources. This will be seriously studied in the context of Korean society and its demands on criminal justice institutions, first of all the police. Considering that the new technology of big data should be adopted as a way of problem-solving, human rights protection, esp. for the minority in Korean society will have first priority in any big data-based criminal justice policy. In conclusion, criminal policy both to regulate big data technology and to adopt it for crime prevention take practical approach in making decision for the social limits in the development of technology and for the interest of citizen’s safety.