Identifying Early Help Referrals For Local Authorities With Machine
  Learning And Bias Analysis
Eufrásio de A. Lima Neto Jonathan Bailiss Axel Finke Jo Miller Georgina Cosma
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.