- Law enforcement uses AI-powered facial recognition to match faces in surveillance photos or video to known databases.
- Companies like Clearview AI supply massive image databases that police can use to identify suspects.
- A Washington Post investigation found that some police departments have made arrests based solely on AI facial-recognition matches, without solid corroborating evidence.
- “Automation bias” is a problem: officers may over-trust AI matches, even when quality of the source image is poor.
- AI is helping crime labs process evidence faster, for example in complex DNA mixture analysis.
- According to the DOJ & law-enforcement-focused reports, AI tools are used to prioritize digital evidence, sift through massive data loads (e.g., seized phones, emails), and detect relevant patterns.
- In digital forensics, AI can help structure and analyze huge volumes of data more efficiently than humans alone.
- Video AI is used to enhance grainy surveillance footage, reconstruct crime scenes, and simulating events, helping to identify suspects or clarifying what happened.
- Object and activity detection in video feeds (like recognizing suspicious behavior) is being explored.
- AI models can analyze historical crime data to identify potential hotspots or likely criminal networks.
- There are academic frameworks (e.g., CrimeGAT) using graph neural networks to model criminal networks, giving law enforcement insights into relationships and potential future crimes.
- There are early systems like the Language Model-Augmented Police Investigation System (LAPIS) that use large language models to assist officers with legal reasoning during investigations.
- Some firms like Parabon NanoLabs use AI to generate 3D facial images from crime-scene DNA. These “Snapshot Phenotype Reports” attempt to predict characteristics like skin color, hair, and facial structure from genetic markers.
- In some cases, law enforcement has tried to run those AI-predicted faces through facial recognition systems to generate suspect leads.
- However, this technique is controversial: reliability is questioned, and civil liberties advocates warn about misidentification risk.
- Some police departments are experimenting with AI chatbots to help write incident reports. For instance, officers in Oklahoma City used AI to draft crime reports from bodycam audio, radio chatter, and other sources.
Planned / Emerging Uses of AI (or Where AI Is Expanding)
- According to the National Institute of Justice, future AI applications could involve video analytics + facial recognition + activity/object detection to detect crimes in real time and alert law enforcement.
- This could potentially allow more proactive responses (e.g., detecting a violent crime unfolding).
- Ongoing research is looking at applying AI to trace evidence, crime scene reconstruction, medical / injury evaluation, and latent print (fingerprint) analysis.
- Automating or accelerating analysis could reduce backlog and help labs process more cases.
- Researchers have proposed frameworks like MULTI-CASE, which is a transformer-based, ethics-aware, multimodal intelligence system for investigations. It’s designed to combine heterogeneous data (text, images, networks) and give human investigators transparency and explainability.
- Advancing on CrimeGAT, future systems could better predict how criminal networks evolve, who the key players are, and where law enforcement should focus.
- These tools may help not just in identifying suspects, but in anticipating organized crime structures.
- Systems like LAPIS could become more broadly used: AI providing legal reasoning support, helping officers decide on investigative steps, how to conduct interviews, what statutes or legal boundaries apply.
- These systems could potentially reduce errors, but also raise questions about over-reliance and accountability.
- Use of AI to interpret more complex genetic data (beyond just face prediction) — like ancestry, health risks, or behavioral traits — might expand, though this is ethically and legally very controversial.
- AI could potentially assist in building more accurate composite images or profiles from DNA, but regulation and scientific validation are big hurdles.
Key Risks & Ethical Concerns
- Bias: Many AI systems (especially facial recognition) have higher error rates for people of color.
- Privacy: Using AI for mass surveillance raises major civil liberties concerns.
- False Positives / Wrongful Arrests: Over-reliance on AI matches without corroborating evidence can lead to mistaken arrests.
- Transparency: Many AI models are proprietary (“black box”), making it hard to challenge their decisions in court.
- Accountability: Who is responsible when AI is wrong — the software vendor, the law enforcement agency, or the individual officers?
- Regulation: There is no consistent national regulation in many countries; policies vary.
- Ethical Use of Genetic Data: Predicting physical traits from DNA (phenotyping) treads into dangerous territory regarding privacy, consent, and potential misuse.
Bottom Line
- AI is already being used in serious crime investigations (including murders), especially for identification (facial recognition), forensic processing, and data analysis.
- More advanced and ambitious uses — like real-time crime detection, integrated investigative intelligence systems, and predictive models for criminal networks — are in development or being piloted.
- But significant caution is needed: the risks of bias, privacy violations, wrongful arrests, and lack of transparency are very real.
