Detectives are now using AI to crack long - standing cold cases. In the UK, police recently announced that an AI tool named S?ze has successfully restarted the investigation of a 40 - year - old unsolved case. This breakthrough not only shows the potential of artificial intelligence in criminal investigation but also triggers global in - depth discussions on technological ethics and judicial fairness. This article will analyze the core technologies of AI detective work, typical cases, technological limitations, and future trends, giving you a comprehensive understanding of the judicial revolution behind it.
Technologies at the Core of AI - Based Criminal Investigation
1. Multimodal Data Analysis
AI systems can build a panoramic view of a case by integrating heterogeneous information such as video surveillance, social media records, and financial data. For example, the S?ze tool used by the UK police can analyze massive data from 27 cases within 30 hours, while manual work by traditional investigators may take decades. Its core technologies include:
| Aspect | Function |
|---|---|
| Natural Language Processing (NLP) | Parse hidden information in witness testimonies, such as time contradictions or emotional fluctuations. |
| Image Enhancement Algorithm | Enhance blurry surveillance images to 4K resolution to identify key details such as license plates and fingerprints. |
| Behavior Pattern Recognition | Use machine learning to predict suspect movement trajectories, such as the spatiotemporal patterns of serial theft cases. |
2. Predictive Crime Modeling
Based on historical data, AI models can predict high - crime areas and time periods. For example, the AI system used by the Los Angeles police has increased the clearance rate of robbery cases by 37%. Its operating mechanism includes:
| Aspect | Details |
|---|---|
| Spatiotemporal Clustering Analysis | Identify the correlation between crime hotspots and population movement. |
| Social Network Analysis | Track the communication patterns and capital flows of criminal gangs. |
Landmark Cases Solved by AI
1. UK 40 - Year Cold Case: Breakthrough of the S?ze Tool
In September 2024, the Avon and Somerset police of the UK used S?ze to analyze a 1984 murder case. The tool achieved a breakthrough through the following steps:
Data Integration: Scan over 500 hours of video tapes and 200,000 pieces of paper archives.
Voice Comparison: Match oral recordings of suspects' relatives and friends with background sounds at the time of the crime to confirm the authenticity of key testimonies.
Digital Forensics: Extract fingerprints from faded checks and match them with the database to discover new clues.
2. US "Golden State Killer" Case: Synergy of Genetic Data and AI
In 2018, investigators used AI to analyze 200,000 pieces of genetic data and identified 72 - year - old suspect Joseph James DeAngelo. This case marks the following:
Pedigree Database Mining: Reverse - build a family tree through publicly available genetic information.
Timeline Reconstruction: Simulate the overlap between the crime scene and the victim's activity trajectory with AI.
Technological Limitations and Ethical Controversies
1. Risk of Miscarriage of Justice and Algorithmic Bias
Data Bias: If the training data set focuses on a specific ethnic group, it may lead to incorrect associations (such as misjudging by skin color).
Case Warning: In a wrongful arrest case in the US in 2023, AI misidentified the suspect due to ignoring dialect differences.
2. Conflict between Privacy Rights and Judicial Transparency
Surveillance Abuse: The over - use of facial recognition technology in public places has raised public concerns.
Black - Box Problem: AI decision - making lacks interpretability and is difficult to meet the requirements of judicial transparency.
Future Trends: Human - Machine Collaborative Intelligent Judiciary
1. Hybrid Investigation Mode
| Aspect | Details |
|---|---|
| AI Pre - screening + Manual Review | For example, the "AI preliminary screening system" of the Tokyo Metropolitan Police Department reduces the case - handling time by 60%. |
| Virtual Reality Reconstruction | Reconstruct crime scenes in 3D to assist the jury in understanding the case. |
2. Global Technical Standard Setting
EU AI Act: Requires judicial AI to pass the transparency certification.
IEEE Ethical Framework: Proposes seven core principles of "Explainable AI" (XAI).
