<?xml version="1.0" encoding="UTF-8"?><ns2:project xmlns:ns1="http://gtr.rcuk.ac.uk/gtr/api" xmlns:ns2="http://gtr.rcuk.ac.uk/gtr/api/project" xmlns:ns3="http://gtr.rcuk.ac.uk/gtr/api/fund" xmlns:ns4="http://gtr.rcuk.ac.uk/gtr/api/person" xmlns:ns5="http://gtr.rcuk.ac.uk/gtr/api/project/outcome" xmlns:ns6="http://gtr.rcuk.ac.uk/gtr/api/organisation" ns1:created="2026-06-03T15:52:43Z" ns1:href="http://gtr.ukri.org/gtr/api/projects/BFB62F2B-E304-407F-B293-B48879FF2001" ns1:id="BFB62F2B-E304-407F-B293-B48879FF2001"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/4BF11012-EB74-4E4D-9713-39368A066D3C" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/6DDAE634-34C8-4D9C-AA02-0D9193603F3C" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/6DDAE634-34C8-4D9C-AA02-0D9193603F3C" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2020-06-29T23:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/765518C5-08BF-4A2F-9DA6-1F3F7CBCA72E" ns1:rel="FUND" ns1:start="2020-03-01T00:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">971715</ns2:identifier></ns2:identifiers><ns2:title>FourAI: Four Point AI System for CCTV Security</ns2:title><ns2:status>Closed</ns2:status><ns2:grantCategory>Small Business Research Initiative</ns2:grantCategory><ns2:leadFunder>Innovate UK</ns2:leadFunder><ns2:abstractText>The current project builds upon our prior research to deliver a market-ready AI product which enhances safety and reduces delay minutes caused by dangerous, suspicious or anti-social behaviour in a railway environment. The scope proposes to radically change the way Network Rail delivers its mission to connect people safely on time, while developing a capability that fills a void in the marketplace for interpretable and inclusive AI which safeguards that algorithmic decision-making does not unfairly single out protected demographics. The innovation seeks to save time, enable faster response, and push the state-of-the-art in the industry by enabling transparent, explainable and accountable AI models that reduce negative externalities due to bias and unfairness.</ns2:abstractText></ns2:project>