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

“风险中的风险”：重塑人工智能算法以预测虐待儿童行为。

• 批准号: ES/R00983X/1
• 负责人: Stephen Parker
• 金额: $ 25.65万
• 依托单位: University of the West of England
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=ES%2FR00983X%2F1
• 关键词: risk; remodelling; artificial; intelligence; algorithms

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.

英国的儿童保护在很大程度上取决于风险预测，这是自1980年代后期以来英国日益兴趣的领域（Browne＆Saqi 1988，Creighton 1992）。通常将虐待儿童的虐待儿童视为公理，应通过风险预测来检测，以确定孩子可能被虐待或被忽视的脆弱和危险家庭。在早期识别此类家庭的目的是针对早期干预，以减少虐待风险。为了满足这种需求，各个地方当局委员会算法风险预测系统来自获利提供商。该提议的项目解决的问题是，鉴于纵向数据显示儿童保护结果的准确性差，并且在风险预测中，这种系统是否适合目的”。最近，家庭部门主席强调了这一问题（Munby，2016年）。该拟议的项目通过研究一种新方法来解决以一种更现实的方式来预测风险的可能性，以提供更好的方式，从而为他们提供更好的儿童保护系统，而不是必须在潜在的不准确数据上工作。它提出了一种整理，评估和提取先前研究的一致信息并以一致和可靠的方式进行测试的新的和变革性的方法。潜在的潜在范围用于将算法风险预测转移到新领域的新系统；现有系统不会从这些错误中“学习”，因此技术在算法预测的阶段停滞而不是发展为基于证据的，可靠和响应迅速的人工智能（AI）（AI）。关键的研究问题/目标是： - 在儿童保护中对现有风险研究中的现有风险研究中的一种归一化置信度限制；用于开发新的风险的应用程序，以促进其设计，以促进其设计，以置于新的风险; - 在设计中的应用程序，并设计了一种;符合GDPR的AI风险预测模型，适用于前后儿童保护工作。这项研究的方法是变革性的，结合了传统和开创性方法的混合。该方法的每个阶段均已评估了其方法和/或结果中潜在转化的水平。团队将通过创建先前相关研究的第一个，全面和可重复使用的数据库来启动拟议项目。该研究的其余部分采用的创造性和新方法是更高的风险，但是如果成功将获得相应高的奖励。在创建了研究数据库之后，团队将分析其特征，大小，范围和方法，以应用一致的计算功率比率，创建比较分析，包括优势，劣势和置信度限制。这些结果将在埃格斯顿（Eggleston）的工作中使用贝叶斯统计数据在实际发现过程中的使用方面进行分析（Eggleston 1983）。贝叶斯网络提供了一种新颖的方法，以建立社会和技术问题的证据加权标准，包括推理（使用贝叶斯推理算法），学习（使用期望 - 最大化算法），计划（使用决策网络）和感知（使用动态贝叶斯网络）。概率算法也可以用于过滤，预测，查找数据流的解释以及帮助系统随着时间的推移分析过程。在这种情况下，我们将提供跨风险因素的信心及其证据概念的一致度量。我们方法的核心变革性要素将使风险预测系统的范围范围来考虑优势和劣势，包括识别差距，为法院提供可靠的法律指标，即适当的权重作为项目结果。