ML enabled Risk Identification & Assessment in Child Exploitation

机器学习支持风险识别

• 批准号: 32807
• 金额: $ 47.1万
• 依托单位: TRILATERAL RESEARCH LTD
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=32807
• 关键词: ML; enabled; Risk; Identification; Assessment

This project aims to significantly advance risk assessment practices in public and private sector organisations by applying machine learning techniques to the analysis of big data.In collaboration with the police and wider agencies supporting the local safeguarding children's board, this project will create disruptive algorithms to power STRIAD, our cloud-based data-driven risk assessment platform.This project will address a use case that focuses on the computation of risk and vulnerability which is pivotal to protecting young people exposed to the threats of sexual and criminal exploitation and to identify those likely to reoffend.STRIAD will be easily accessible, adaptable and scalable and will boost inter-agency cooperation, allowing organisations to pool data in an ethical, secure and privacy-considerate manner, enabling ground-breaking analysis and rapid identification of risk.Advanced data-driven risk identification and risk assessment results in informed service budget planning, and optimized allocation of resources. Most of all, it empowers early intervention and reduces the likelihood of harm to the most vulnerable in society.

该项目旨在通过将机器学习技术应用于大数据分析，显著推进公共和私营部门组织的风险评估实践。通过与警方和支持当地儿童保护委员会的更广泛机构合作，该项目将创建颠覆性算法，为STRIAD提供动力，STRIAD是我们基于云的数据驱动风险评估平台。该项目将解决一个用例，重点关注风险和脆弱性的计算，这对于保护面临性剥削和犯罪剥削威胁的年轻人以及识别可能再次犯罪的年轻人至关重要。STRIAD将易于访问、适应和扩展，并将促进机构间合作，使组织能够以道德、安全和考虑隐私的方式汇集数据，从而实现突破性的分析和快速识别风险。先进的数据驱动的风险识别和风险评估结果在知情的服务预算规划，并优化资源配置。最重要的是，它使早期干预成为可能，并减少对社会中最弱势群体造成伤害的可能性。