Ethics Review of Machine Learning in Children's Social Care.
What Works for Children's Social Care. Alan Turing Institute. Rees Centre for Research in Fostering and Education.
Grantee Final Report
Published: January 2020
What Works for Children's Social Care
This report reviews the ethics of using machine learning (ML) in children’s social care (CSC) in the United Kingdom. This research is informed by a literature review, an examination of existing ethical frameworks in social care and ML, a stakeholder roundtable with 31 participants, and a workshop with 10 CSC family members. Findings are discussed in three tiers that address whether ML should be used, can ML be used in a responsible way, and the potential of data scientific insights to transform the future of CSC. An integrated ethical framework for the use of ML in CSC is presented and includes ethical values that set the direction of travel for the responsible use of ML in CSC, practical principles that establish the moral justifiability of the integrated practices of social care and ML innovation, and professional virtues that establish common principles of professional integrity shared by social work and responsible ML innovation. Steps for the design and implementation pipeline of the production and use of ML models in CSC are discussed, and eight recommendations are made. These recommendations include: mandate the responsible design and use of ML models in CSC at the national level; connect practitioners and data scientists across local authorities to improve ML innovation and to advance shared insights in applied data science through openness and communication; institutionalize inclusive and consent-based practices for designing, procuring, and implementing ML models; fund, initiate, and undertake active research programs in system, organization, and participant readiness; understand the use of data in CSC better; use data insights to describe, diagnose and analyze the root causes of the need for CSC; focus on individual- and family-advancing outcomes, strengths-based approaches, and community-guided prospect modeling; and improve data quality and understanding through professional development and training. Numerous references.
United Kingdom; child welfare reform; information technology; risk factors; identification; ethics; professional conduct; child welfare workers; COMPUTER PROGRAMS; Management information systems