Local authorities in England, such as Leicestershire County Council (LCC), provide Early Help services that can be offered at any point in a young person's life when they experience difficulties that cannot be supported by universal services alone, such as schools. This paper investigates the utilisation of machine learning (ML) to assist experts in identifying families that may need to be referred for Early Help assessment and support. LCC provided an anonymised dataset comprising 14360 records of young people under the age of 18. The dataset was pre-processed, machine learning models were build, and experiments were conducted to validate and test the performance of the models. Bias mitigation techniques were applied to improve the fairness of these models. During testing, while the models demonstrated the capability to identify young people requiring intervention or early help, they also produced a significant number of false positives, especially when constructed with imbalanced data, incorrectly identifying individuals who most likely did not need an Early Help referral. This paper empirically explores the suitability of data-driven ML models for identifying young people who may require Early Help services and discusses their appropriateness and limitations for this task.

翻译：英国地方当局（如莱斯特郡议会）提供早期帮助服务，可在青少年遭遇无法仅通过学校等通用服务支持的困难时，随时介入其生活。本文探究利用机器学习辅助专家识别可能需要被推荐接受早期帮助评估与支持的家庭。莱斯特郡议会提供了一个包含14360条18岁以下青少年记录的去标识化数据集。我们对数据集进行了预处理，构建了机器学习模型，并通过实验验证和测试模型性能。应用偏差缓解技术以提升这些模型的公平性。测试期间，尽管模型展现了识别需要干预或早期帮助的青少年的能力，但它们在基于不平衡数据构建时产生了大量假阳性结果，错误地识别了极可能并不需要早期帮助转介的个体。本文通过实证探索数据驱动型机器学习模型在识别可能需要早期帮助服务的青少年方面的适用性，并讨论了其在此任务中的适宜性与局限性。