Ethics Review of Machine Learning in Children’s Social Care

Legacy Content

This project or publication was produced before or during the merger of What Works for Children’s Social Care (WWCSC) and the Early Intervention Foundation (EIF).

January 6, 2020

Ethics Review of Machine Learning in Children’s Social Care

Report summary

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Full report

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Summary

This report:

Reviews the ethical criteria that would make the use of machine learning (ML) in children’s social care (CSC) justifiable and examines the problematic contexts in which such criteria may not be met;
Identifies requirements and best practice for the responsible use of ML in CSC;
Presents recommendations for a way forward.

Aims

The aim of the report is to answer the question: “Is it ethical to use machine learning approaches in children’s social care systems and if so, how and under what circumstances?”. The findings are aimed at data scientists, policy makers, local authority (LA) children’s services departments, civil servants, and citizens.

Method

Following a request for proposals, we commissioned The Alan Turing Institute and the Rees Centre, University of Oxford to undertake the review. The research is informed by a review of the literature, the integration of multiple existing ethical frameworks in social care and ML, a stakeholder roundtable with 31 participants, and a workshop with 10 family members who have lived experience of children’s social care.

The Turing and the Rees Centre mapped out common motivations and moral foundations to propose a list of ethical values, practical principles and professional virtues which can be used as guardrails for the responsible use of ML in CSC. The aim of presenting these practical ethics is that they can be actively adopted by all affected stakeholders as a vehicle of common commitment to the shared purpose of using these technologies exclusively in ways that advance public wellbeing and benefit society.

To answer the question whether ‘Can we do this right?’, The Turing and the Rees Centre, present standards for best practice across ML’s design and deployment lifecycle, paying special attention at each step of the way to the CSC context. They cover the data quality and use, model design and implementation.

Implications for future research

The report should be utilised both as a means to reflect on questions about the appropriateness and justifiability of using ML applications in CSC (both for specific use cases and in general) and as a preliminary guide for developing projects involving ML in CSC.