Identifying Early Help Referrals For Local Authorities With Machine Learning And Bias Analysis
This addresses the challenge of efficiently allocating social services for vulnerable youth, but it is incremental as it applies existing ML methods to a new dataset with bias analysis.
This paper tackled the problem of using machine learning to identify families needing Early Help referrals for young people in local authorities, finding that while models could identify those requiring intervention, they produced significant false positives, especially with imbalanced data.
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.