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Identifying early help referrals for local authorities with machine learning and bias analysis

Author

Listed:
  • Eufrásio de A. Lima Neto

    (De Montfort University)

  • Jonathan Bailiss

    (Loughborough University)

  • Axel Finke

    (Loughborough University)

  • Jo Miller

    (Leicestershire County Council)

  • Georgina Cosma

    (Loughborough University)

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 14 360 records of young people under the age of 18. The dataset was pre-processed, ML models were developed, 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.

Suggested Citation

  • Eufrásio de A. Lima Neto & Jonathan Bailiss & Axel Finke & Jo Miller & Georgina Cosma, 2024. "Identifying early help referrals for local authorities with machine learning and bias analysis," Journal of Computational Social Science, Springer, vol. 7(1), pages 385-403, April.
    • Handle: RePEc:spr:jcsosc:v:7:y:2024:i:1:d:10.1007_s42001-023-00242-7
    DOI: 10.1007/s42001-023-00242-7
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    References listed on IDEAS

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    1. Meherun Nesa & Tumpa Rani Shaha & Young Yoon, 2022. "Prediction of juvenile crime in Bangladesh due to drug addiction using machine learning and explainable AI techniques," Journal of Computational Social Science, Springer, vol. 5(2), pages 1467-1487, November.
    2. Scott Thiebes & Sebastian Lins & Ali Sunyaev, 2021. "Trustworthy artificial intelligence," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 447-464, June.
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