Identifying Early Help Referrals For Local Authorities With Machine Learning And Bias Analysis

TL;DR

This study evaluates machine learning models for early help referral identification in local authorities, highlighting their potential and limitations, especially regarding bias and false positives in imbalanced datasets.

Contribution

It demonstrates the application of ML models with bias mitigation techniques to identify at-risk youth for early intervention in a real-world setting.

Findings

Models can identify at-risk youth but have high false positive rates.

Bias mitigation improves fairness but does not eliminate false positives.

Imbalanced data affects model performance and fairness.

Abstract

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.