Clearview AI Tests InquiryIQ to Automate Police Web Searches Across 70 Billion Images
Updated
Updated · WIRED · Sep 10
Clearview AI Tests InquiryIQ to Automate Police Web Searches Across 70 Billion Images
1 articles · Updated · WIRED · Sep 10
Summary
InquiryIQ is an unreleased Clearview AI prototype that would take details from a face-recognition hit and automatically search the web, analyze images, and assemble profiles of possible employers, aliases, associates, addresses, and arrest history.
Code reviewed by WIRED shows the tool can use age, gender, and race to make “smarter decisions,” build a “Candidate Graph” of identities and connections, and test multiple models including xAI and Amazon Bedrock during internal evaluation.
Clearview says no law-enforcement customer has used InquiryIQ and that it is not planned for release in its current form, framing it as a way to automate searches detectives already do rather than as an autonomous investigator.
Privacy and defense experts say such systems could compress days of detective work into minutes but also cheapen broad surveillance, obscure why one lead was pursued over another, and amplify hallucinations or online bias in probable-cause decisions.
The experiment extends Clearview’s long-running expansion beyond face matching alone; the company says its database has grown from more than 3 billion scraped images in 2020 to well over 70 billion and is used by 2,000-plus U.S. agencies.
While critics fear AI surveillance, could this system's digital audit trail actually expose the hidden biases of human detectives?
If an AI silently builds a criminal profile on you using scattered web data, how would you ever prove it wrong?
AI-Driven Policing in 2026: The Technical, Ethical, and Legal Fallout of InquiryIQ, Clearview AI, and Automated Surveillance
Overview
In 2026, Clearview AI launched InquiryIQ, an AI tool that automates police investigations by rapidly collecting and connecting online data to build detailed personal profiles. This technology eliminates traditional investigative friction, making broad surveillance and 'fishing expeditions' much easier for law enforcement. As a result, automation bias grows—officers increasingly trust algorithmic outputs, leading to wrongful arrests and privacy risks. The mass scraping of public data and lack of strong legal safeguards erode privacy and public trust, while fragmented regulations struggle to keep pace. These developments highlight urgent ethical, legal, and societal challenges in the age of AI-driven policing.