Machine learning is revolutionizing the way we detect issues within police forces, allowing for quicker action and safer communities. A new proof-of-concept tool has been developed using crime data to provide an early warning of potential problems, enabling swifter responses from inspectors.
His Majesty’s Inspectorate of Constabulary and Fire & Rescue Services conducts regular assessments of police forces’ effectiveness, efficiency, and legitimacy. However, when a serious issue is identified and the response is deemed insufficient, a force is escalated into enhanced monitoring, known as Engage, which may impact public service.
In a proactive move to address these concerns sooner, the Inspectorate approached the Accelerated Capability Environment to develop an early-warning predictor tool. This tool aims to estimate assessment grades before inspections, prioritizing visits to forces flagged for potential problems in order to improve issues promptly and enhance community safety.
Focusing on how well forces investigate crime, the Inspectorate collaborated with The London Data Company to create a machine-learning algorithm using various crime-related data sources. In just eight weeks, a proof of concept was developed, correctly predicting force grades in 60% of cases and close to the mark in 90% of cases.
Jacquie Hayes, insight portfolio director, highlighted the alignment between the tool’s conclusions and the Inspectorate’s assessment process, emphasizing the potential for further data utilization and expansion to other assessment questions. With ambitions to become more data-driven, the Inspectorate is working on deploying the tool into live systems and improving the overall inspection process within the next 18 months.
Looking ahead, potential applications extend beyond policing to include fire and rescue services, reflecting the initiative’s broad scope and future potential. While artificial intelligence cannot replace human inspection teams, it offers valuable insights into potential issues, prompting further investigation to identify and rectify underlying problems, ultimately enhancing community safety.
In conclusion, the collaboration between the Inspectorate and The London Data Company represents a significant step forward in leveraging technology to enhance inspection processes and support forces in addressing challenges promptly. By combining machine learning with human expertise, the initiative aims to create safer communities by addressing issues proactively and facilitating continuous improvement within policing and emergency services.
