{"id":"AIBD-0677","title":"Queensland Audit Office reports that the MPST camera AI cut the photo assessments needing human review by 98.7% in 2024","summary":"The Queensland Audit Office reports that in 2024 the MPST image-recognition AI cut the assessments needing human review at its vendor by 98.7%, to 2.7 million.","date_observed":"2024-01-01","date_basis":"event","date_precision":"year","benefit_status":"reported","source_collection":null,"source_entry":null,"account":"Queensland's Department of Transport and Main Roads (TMR) runs cameras that detect drivers using mobile phones or not wearing seatbelts. An external vendor owns and manages the image-recognition AI and the devices. A ministerial answer to the Queensland Parliament names Acusensus as TMR's contractor for this technology. The Queensland Audit Office describes the system as using machine learning and computer vision. The AI filters out photos that are unlikely to show offenses. Staff at the vendor review the rest, and the Queensland Revenue Office reviews each possible offense before a fine is issued. In a report tabled on 24 September 2025, the auditor compiled figures for 2024. The AI made 208.4 million assessments, the vendor reviewed 2.7 million and the revenue office reviewed 137,000 possible offenses. About 114,000 fines followed, including fines later withdrawn. The auditor found that TMR had controls for accuracy and privacy but had not yet done a full ethical risk assessment of the system. The auditor gives no figure for offenses that the AI missed.","category":"aggregate result","unexpected":false,"purpose":"Acusensus built the system to find possible mobile phone and seatbelt offenses in roadside camera photos for Queensland's transport department.","system":"Mobile Phone and Seatbelt Technology (MPST) image-recognition AI","model_version":null,"developer":"Acusensus, the external vendor that owns and manages the AI (named as TMR's contractor in a ministerial answer, while the audit names no vendor)","deployer":"Queensland Department of Transport and Main Roads, with the Queensland Revenue Office reviewing possible offenses","location":"Queensland, Australia","domain":"Traffic enforcement (road safety)","segment":null,"benefit_type":"Repetitive Task Automation","work_activities":["4.A.1.b.1","4.A.2.a.3"],"taxonomy_note":null,"registry_ids":{},"ratings":[{"group":"organizational","depth":1,"reason":"Reviewers at the vendor checked 2.7 million of the 208.4 million assessments that the AI made in 2024, a 98.7% cut in review work."},{"group":"societal","depth":null,"reason":"The audit counts fines, and it does not measure whether the cameras reduced crashes or injuries."}],"ai_role":{"status":"unassessed","statement":"The 98.7% figure compares the assessments that the AI made with those that people still reviewed. The auditor does not compare the program with one run without AI or measure how many offenses the AI missed. The auditor describes the system as using machine learning and computer vision."},"evidence":[{"kind":"audit report","citation":"Queensland Audit Office. Managing the ethical risks of artificial intelligence. Report 2: 2025–26, tabled 24 September 2025, chapter 5 (archived copy of the report page).","title":"Managing the ethical risks of artificial intelligence (Report 2: 2025–26)","url":"https://web.archive.org/web/20260713131003/https://www.qao.qld.gov.au/reports-resources/reports-parliament/managing-ethical-risks-artificial-intelligence","date":"2025-09-24","written_by":"independent","quote":"In 2024, the AI system reduced the volume of assessments requiring human review at the external vendor by 98.7 per cent to 2.7 million."},{"kind":"other","citation":"Queensland Parliament. 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Managing the ethical risks of artificial intelligence. Report 2: 2025–26, tabled 24 September 2025, chapter 5 (archived copy of the report page).","title":"Managing the ethical risks of artificial intelligence (Report 2: 2025–26)","url":"https://web.archive.org/web/20260713131003/https://www.qao.qld.gov.au/reports-resources/reports-parliament/managing-ethical-risks-artificial-intelligence","date":"2025-09-24","written_by":"independent","quote":"In 2024, the AI system reduced the volume of assessments requiring human review at the external vendor by 98.7 per cent to 2.7 million."},{"kind":"other","citation":"Queensland Parliament. 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An external vendor owns and manages the image-recognition AI and the devices. A ministerial answer to the Queensland Parliament names Acusensus as TMR's contractor for this technology. The Queensland Audit Office describes the system as using machine learning and computer vision. The AI filters out photos that are unlikely to show offenses. Staff at the vendor review the rest, and the Queensland Revenue Office reviews each possible offense before a fine is issued. In a report tabled on 24 September 2025, the auditor compiled figures for 2024. The AI made 208.4 million assessments, the vendor reviewed 2.7 million and the revenue office reviewed 137,000 possible offenses. About 114,000 fines followed, including fines later withdrawn. The auditor found that TMR had controls for accuracy and privacy but had not yet done a full ethical risk assessment of the system. 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An external vendor owns and manages the image-recognition AI and the devices. A ministerial answer to the Queensland Parliament names Acusensus as TMR's contractor for this technology. The Queensland Audit Office describes the system as using machine learning and computer vision. The AI filters out photos that are unlikely to show offenses. Staff at the vendor review the rest, and the Queensland Revenue Office reviews each possible offense before a fine is issued. In a report tabled on 24 September 2025, the auditor compiled figures for 2024. The AI made 208.4 million assessments, the vendor reviewed 2.7 million and the revenue office reviewed 137,000 possible offenses. About 114,000 fines followed, including fines later withdrawn. The auditor found that TMR had controls for accuracy and privacy but had not yet done a full ethical risk assessment of the system. The auditor gives no figure for offenses that the AI missed.","replication":{"produced_by":["Cameras photographed passing vehicles, and the AI assessed each photo for phone use and seatbelt offenses.","Staff at the vendor and then at the Queensland Revenue Office reviewed each possible offense before a fine.","Weekly audits and quarterly performance reviews fed back into the vendor's AI."],"must_hold":["A person must check every possible offense before a fine.","The contract must set performance indicators for the AI's accuracy."],"echoes":[]}}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["account","replication"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"}},"summary":{"total":91,"addressed":91,"known":28,"reviewed":2,"reviewed_complete":2,"reviewed_partial":0,"record_mapped":26,"review_not_assessed":63,"not_assessed":0,"complete":false,"by_state":{"value_present":28,"not_reported":0,"unresolved":63,"not_assessed":0,"not_applicable":0,"withheld":0,"source_unavailable":0}}},"presentation_version":"caba032ee950a325f774a9c74eee16fec5f56dcb25dad1a7cba82c0ae3a2b434"}