ML enabled Risk Identification & Assessment in Child Exploitation

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。该项目将与警方和支持当地保护儿童委员会的更广泛机构合作，创建颠覆性算法来支持我们基于云的数据驱动的风险评估平台STRIAD。该项目将解决一个用例，该用例侧重于风险和脆弱性的计算，这对于保护性剥削和犯罪威胁下的年轻人以及识别可能再次犯罪的人至关重要。STRIAD将易于访问、适应性和可扩展性，并将促进机构间的合作，使组织能够以道德、安全和隐私-以体贴的方式，实现开创性的风险分析和快速识别。先进的数据驱动的风险识别和风险评估有助于制定明智的服务预算规划，并优化资源分配。最重要的是，它使早期干预成为可能，并降低了对社会中最脆弱的人造成伤害的可能性。

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.