The 'risk of risk': remodelling artificial intelligence algorithms for predicting child abuse.

Child protection in the UK relies heavily on risk prediction, an area of growing interest in the UK since the late 1980s (Browne & Saqi 1988, Creighton 1992). It is generally taken as an axiom that child abuse can and should be detected via risk prediction to identify vulnerable and risky families whose children may become abused or neglected. The purpose of identifying such families at an early stage is to target early intervention towards them to reduce the risk of abuse. To service this need, individual local authorities commission algorithmic risk prediction systems from profit making providers. The question this proposed project addresses is whether such systems are 'fit for purpose' given the concerning longitudinal data showing poor accuracy in child protection outcomes and an unacceptably high number of false positives and false negatives in risk prediction. This concern was recently highlighted by the President of the Family Division (Munby 2016).

This proposed project addresses the issue by looking at the possibilities for a new method of predicting risk in a more realistic way that provides a better means for child protection systems to be supported by them, rather than have to work potentially inaccurate data. It sets out a new and transformative means of collating, assessing and extracting consistent information from previous studies and testing them in a consistent and reliable way. The potential exists for scoping a new system which moves algorithmic risk prediction into new territory; existing systems do not 'learn' from these errors so the technology stalls at the stage of algorithmic prediction rather than developing into evidenced-based, reliable and responsive artificial intelligence (AI).

The key research questions/objectives are:
- What is a normalised confidence limit(s) in existing risk prediction studies in child protection;
- To develop a new method of calculating risk, and design for its application in child protection;
- To assess the possibility of designing a model for a new, GDPR-compliant, AI model of risk prediction suitable for use in pre- and post-proceedings child protection work.

This study's methodology is transformative, bringing together a mix of traditional and pioneering methods. Each stage of the methodology has been assessed for the level of potential transformation in either its approach and/or outcome. The team will start the proposed project by creating the first, comprehensive and re-usable database of previous relevant studies. The creative and new methods employed by the rest of the study is higher risk, but if successful will yield a correspondingly high reward. Having created the database of studies, the team will analyse their characteristics, size, scope and methods to apply a consistent means of calculating their power ratio, creating a comparative analysis including strengths, weaknesses and confidence limits. These results will be analysed using Bayesian statistics in the context of Eggleston's work in respect of the use of probability in fact finding processes (Eggleston 1983). Bayesian networks provide a novel means of establishing criteria for weighting of evidence for social and technical problems including reasoning (using the Bayesian inference algorithm), learning (using the expectation-maximization algorithm), planning (using decision networks) and perception (using dynamic Bayesian networks). Probabilistic algorithms can also be used for filtering, prediction, finding explanations for datastreams, and helping systems to analyse processes over time. Used in this context, we will provide a consistent measure of confidence across risk-factors and measure of their evidential probity. This core transformative element of our methods will enable scoping of a risk prediction system to take account of strengths and weaknesses, including identifying gaps, providing a reliable legal indicator to courts as to the appropriate weighting as a project outcome.

The impact activities fit into a well-established existing programme already in place. Interim results will be communicated via evidence briefings, reports and workshops. The team work with the Ministry of Justice and the Department of Education (the two government departments collectively responsible for child protection) so will introduce this project into their pre-existing dissemination strategy amongst policy heads. The project's findings will also be directly disseminated across all 152 Local Authorities in England. The team are experienced media commentators with extensive media contacts; two are ESRC media trained. Their previous work is featured in the Times, the Telegraph, the Guardian and on BBC and Channel 4 news and the ESRC's Society Now.

Impact will be maximised through focus on stakeholder engagement during and after the project. The team have a track record of engagement with a large and diverse network having previously successfully run funded research in the field. The team's contacts comprise a range of interlinked academics including authorities in the area such as Professors Judith Masson (Bristol), Judith Harwin (Lancaster), Karen Broadhurst (Lancaster), Viv Cree (Edinburgh), Sian Pooley (Oxford) and Lucy Bowes (Oxford). The team have been invited to work with the Family Policy Team at the Ministry of Justice in relation to research, strategy and policy to reduce the number of children requiring child protection systems, and to advise on how the system operates at each stage.

The project will create a new inter-disciplinary group via three workshops bringing together stakeholders who may not usually have the opportunity to work together to discuss and debate the key themes of the project as it develops. Following completion of the project and publication of the results the group will be well established and will continue as a new network of expertise to maximise dissemination. This is an important element of the planned impact as the research aims to establish and open dialogue in relation to the need for inter-disciplinary perspectives on this topic. An open-access website and social media activities are planned to give regular updates and information about developments in the research, leaflets and information packs will be distributed to interested groups and key government stakeholders who will be invited to comment and engage with the final report.

A primary aim of stakeholder involvement is to bring together existing information, but consider it in a novel and transformative manner to create a new way of considering the research questions. Findings will be of use to those involved in economic policy as well as those concerned with welfare policies. Findings are potentially of global significance in relation to any jurisdiction using a similar child protection system. Given the nature of the project and its potential national and global economic and social impact the aim of a range of partner involvement throughout the project will increase networking and knowledge exchange opportunities.

- Creation of an Ethical Charter for the use of AI in child protection.

- Academic dissemination: At least one 3* and one 4* paper will be produced and a related conference paper.

- Practitioner dissemination: The external engagement activities including the workshops will include an appropriate variety of stakeholders, invited to express their views and be involved throughout the project.

- Public sector and NGO dissemination: Key figures in the MoJ and DfE, the third sector, the judiciary and local authorities will be invited to be involved at all stages of the research, and will receive regular updates and a copy of the final report. The team hold an annual public lecture, hosted at London South Bank University and will disseminate findings via this medium to a wide public audience