MTCT: Using AI and machine learning to help Police Forces automate Out of Court Order administration, streamline the restorative justice process and help reduce the court backlog.

随着刑事司法削减、法律援助问题、大律师罢工和新冠肺炎疫情的出现，英国的法院积压案件已飙升至40多万起，受害者等待正义的时间长达数年。大多数案件是由惯犯犯下的低级罪行，*只有10%的案件会被判监禁。* 90%的案件是针对已经有一次或多次定罪的人提起的，迫切需要替代方案来减轻法庭负担并解决再犯问题。庭外处置（OOCD）是一种解决低级别罪行的方法，当罪犯已知并承认罪行时。oocd的一项驱动原则是通过与受害者协商并允许转移和恢复性司法（RJ）来减少再犯罪。英国最大的OOCD试验最近在伦敦完成。结果表明，通过OOCD处理的用户：*再次犯罪的可能性降低30% * 18-21岁的用户犯罪率降低58% * BAME组用户犯罪率降低34%如果大规模应用，这将产生变革性的结果。然而，OOCD过程是耗时的，并且没有技术来支持官员实施OOCD，也没有评估作为威慑再犯的有效性。这给官员带来了巨大的行政和道德压力，导致在使用东方经合组织方面存在高度的主观性和巨大的区域和种族差异。Make Time Count Today与伦敦大都会警察局和剑桥循证警务中心合作，打算开发一个高度创新的人工智能数据分析平台，以帮助警察部队：1 .审查目前的法庭积压案件，并确定哪些案件可以作为OOCD处理；2 .预测OOCD程序作为防止再犯机制的有效性。3 .精简和自动化OOCD流程中的管理要点；将RJ框架嵌入OOCD系统以改善结果；最终：*减少通过法庭处理的低级犯罪数量*减少通过使用OOCD和RJ再次犯罪。

With criminal justice cuts, legal aid issues, barristers' strikes and COVID, the UK has seen court backlogs spiral to over 400,000 cases and left victims waiting years for justice. Majority of cases will be for low-level offences from repeat offenders,* Only 10% of cases result in custodial sentences.* 90% of all cases brought against people who already have one conviction or more,Alternatives are drastically needed to reduce court burden and address reoffending.An Out of Court Disposal (OOCD) is a method of resolving low-level crime when an offender is known and admits offence. A driving principle for OOCDs is to reduce re-offending by consultation with the victim and enabling diversion and restorative justice (RJ). UK's largest OOCD trial recently completed in London. Result show that users processed via OOCD:* 30% less likely to reoffend* 58% less for those aged 18-21* 34% reduction in offending for users from BAME groupsThis would have transformative results if applied at-scale. However the OOCD process is time-consuming and there's no technology to support Officers implementing OOCDs, nor assess effectiveness as a recidivism deterrent. This puts significant administrative and moral pressure on Officers, resulting in a high-level of subjectivity and huge regional and ethnic disparities in OOCD use.Working with the London MET police and Cambridge Centre for Evidence Based Policing, Make Time Count Today intend to develop a highly innovative AI data analytics platform to help police forces:1. Review current court backlog and determine which cases could be dealt with as an OOCD;2. Predict effectiveness of OOCD process as a mechanism to prevent reoffending vs court,3. Streamline and automate the administrative pinch-points in the OOCD process;4. Embed RJ frameworks into OOCD system to improve outcomes;And ultimately:* Reduce number of low-level offences processed through courts* Reduce re-offending through use of OOCD and RJ.