Make Time Count Today - Reducing criminal reoffending on probation through data analytics, predictive behaviour recognition and optimised interventions

** 解决的问题 ** 犯罪每年给英国经济造成的损失超过580亿英镑，每年有120万人被定罪。其中87%有前科，60%的获释者和30%的缓刑犯在12个月内重新犯罪，重新犯罪并非不可避免。来自挪威和德国等国的证据表明，有针对性的替代办法，如庭外处置令和积极参与初次判刑计划，在降低这些重新犯罪率方面非常有效（-20%的重新犯罪率对60%）。尽管是刑事司法系统武器库的一部分，预算削减使犯罪率增加了24%，这意味着个人有9倍的可能性被告上法庭，而不是被规定这些更有效的替代方案。创新 ** 利用人工智能、机器学习（ML）和以用户为中心的生态系统设计，系统的目标是更频繁地与用户互动，让他们感到得到支持，并通过有针对性的、主动的互动来防止向下螺旋式上升到再次犯罪。该项目是与剑桥循证警务中心合作设计的，并将与该中心合作交付，该中心是剑桥大学的一部分，他在人工智能、数据分析和警务方面的开创性工作产生了一种算法，该算法预测的严重暴力案件数量是目前英国政府罪犯评估系统_（_OASys）风险分类模型预测的六倍。** 在此基础上，我们打算建立一个高度创新的平台，以帮助缓刑和康复的利益相关者;1.使用新的机器学习技术识别那些处于缓刑期的人，这些人的重新犯罪风险最高，比当前的OASys系统准确6倍2。通过我们的支持生态系统，设计和启动量身定制的、有针对性的早期干预措施--轻推、签到、接触时间、支持会议，以防止再次犯罪。促进机构间安全、可靠和方便地共享用户信息和互联网服务提供商。减少与此相关的管理和数据孤岛。通过主动管理、积极推动和接触时间，我们旨在通过为最有可能再次犯罪的个人提供及时支持和指导，减少再次犯罪。**_目标影响：增加接触点的数量;减少行政管理;优化对每个人的干预，以减少重新犯罪的风险。

**Problem Addressed**Crime costs UK economy over £58bn pa, with 1.2mn people convicted annually. Of these, 87% have previous convictions, 60% of released prisoners and 30% on probation reoffend within 12 months.Reoffending is not inevitable. Evidence from countries such as Norway and Germany have shown that targeted alternatives such as Out of Court Disposal orders _(_OOCD) and active engagement in Initial Sentence Plans (ISPs) is highly effective at reducing these reoffending rates (-20% reoffending rate vs 60%).Despite being part of the Criminal Justice systems arsenal, budget cuts have increased crime rates by 24% and meant that individuals for are 9x likely to be resent to court, rather than be prescribed these more effective alternatives.**Innovation**Utilising AI, Machine Learning (ML) and user-centric ecosystem design, the goal of the system to more frequently engage with user, make them feel supported and prevent downward spirals to reoffending through targeted, proactive inter_v_entions.The project has been devised with and will be delivered in partnership with the Cambridge Centre for Evidenced-Based Policing, part of the University of Cambridge, whose pioneering work on Al, data analytics and Policing has resulted in an algorithm that predict six times as many cases of serious violence as the current UK Government's Offender Assessment System _(_OASys) risk classification model predicted.**Advancement on Current Approaches**Building upon this strong evidence base, we intend to develop a highly-innovative platform to help probation and rehabilitation stakeholders to;1. Identify those individuals on probation that are at the highest risk of reoffending using new machine learning techniques, 6x more accurate than the current OASys system2. Design and initiate tailored, targeted early interventions -- nudges, check-ins, contact time, support sessions -- via our support ecosystem, to prevent downwards spirals towards reoffending.3. Facilitate the safe, secure and easy sharing of user information and ISPs between inter-agencies.4. Reduce the administration and data silos associated with these.Through proactive management, positive nudges and contact time we aim to reduce reoffending by providing timely support and steer to individuals most at risk of reoffending.**_Targeted Impacts: Increase the number of touch points; reduce administration; optimise intervention per individual to reduce the risk of reoffending._**