Introduction: Problem Definition or Background
The advancement of cutting-edge technology offers new solutions to crimes that threaten the safety of our society. Artificial Intelligence (AI)-based crime prediction systems, known as 'Pre-Crime,' enable proactive prevention activities by predicting times and locations with high crime potential through data analysis. However, this technology simultaneously raises ethical debates regarding personal data breaches, data bias, and the accuracy of predictions. It is time to anticipate the changes that Pre-Crime systems will bring to the future society and to consider the positive utilization and potential risks of the technology in a balanced manner.
Core Concepts and Principles
Pre-Crime systems analyze vast amounts of data to learn crime occurrence patterns and predict future crime possibilities based on this learning. These systems typically follow these steps:
Data Collection and Analysis
Various data are collected, including past crime records, geographical information, demographics, and social media data. The collected data is analyzed through AI algorithms to identify factors that influence crime occurrence.
Prediction Model Development
A model is developed to predict the likelihood of crime occurrence based on the analyzed data. This model is built using various AI technologies such as Machine Learning (ML) and Deep Learning (DL), and it is continuously improved to enhance prediction accuracy.
Risk Assessment and Intervention
Areas or individuals with a high risk of crime occurrence are identified through the prediction model, and proactive prevention measures are taken, such as strengthening police force deployment and providing social welfare services.
Latest Trends and Changes
In recent years, Pre-Crime systems have been utilized in various fields along with technological advancements. According to AnLab's report, cyber threats are expected to become more sophisticated by 2025, raising the possibility of data leakage and misuse of crime prediction data that exploits Pre-Crime systems. In addition, the Global Organized Crime Index 2025 report points to an increase in non-violent crimes such as financial and cybercrimes, suggesting that this may affect the effectiveness and focus of Pre-Crime strategies. The Global Prison Trends 2025 report shows that the adoption of Pre-Crime systems for crime prevention and recidivism prevention is increasing worldwide, but concerns about data bias and personal data breaches continue to be raised. Discussions on strengthening related regulations are expected to proceed actively in response to these concerns.
Practical Application Plans
Pre-Crime systems are being applied in various ways worldwide. The 'Pre-Cas' crime prediction system analyzes big data integrating public safety and public data with AI to predict crime risk by region and is used for police force deployment and crime prevention activities. Chicago's 'Strategic Subject List (SSL)' in the United States creates and manages a list of people who are likely to commit crimes, and the Durham Police's 'Harm Assessment Risk Tool (HART)' in the United Kingdom is used to predict the risk of recidivism of criminals. While these systems contribute to crime prevention, they also face criticism that they may infringe on individual freedoms and rights.
Expert Suggestions
💡 Technical Insight
Precautions When Introducing Technology: When introducing Pre-Crime systems, data bias must be minimized, and a strong security system for personal data protection must be established. In addition, transparency in prediction results must be ensured, and institutional mechanisms must be established to prevent disadvantages due to false positives.
Outlook for the Next 3-5 Years: With the development of AI technology, Pre-Crime systems are expected to become more sophisticated and spread to various fields. In particular, the use of these systems is expected to increase in new areas such as cybercrime prevention, terrorism prevention, and financial fraud prevention. However, at the same time, social discussions on personal data protection and ethical issues are expected to become more active.
Conclusion
Pre-Crime systems have the potential to bring innovative changes to crime prevention. However, they also pose an ethical dilemma that they may infringe on individual freedoms and rights. With the advancement of technology, the scope of Pre-Crime systems should be determined through social consensus, and solutions to personal data protection and data bias issues should be sought. It is time to strive to build Pre-Crime systems that simultaneously guarantee the safety and freedom of the future society.