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，为CNORAD提供动力，我们的云端数据该项目将解决一个用例，重点是计算风险和脆弱性，这对于保护暴露在危险中的年轻人至关重要。性剥削和犯罪剥削的威胁，并确定那些可能重新犯罪的人。CNOAD将易于访问，适应性强，可扩展，并将促进机构间合作，使组织能够以道德，安全和隐私考虑的方式汇集数据，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导致明智的服务预算规划，优化资源配置。最重要的是，它使早期干预成为可能，并减少了对社会中最弱势群体的伤害。

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.