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/2
• 负责人: Stephen Parker
• 金额: $ 25.51万
• 依托单位: Aston University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=ES%2FR00983X%2F2
• 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.

英国的儿童保护严重依赖风险预测，自20世纪80年代末以来，英国对这一领域的兴趣日益浓厚（Browne & Saqi 1988，Creighton 1992）。一般认为，虐待儿童可以而且应该通过风险预测来发现，以确定儿童可能受到虐待或忽视的脆弱和危险家庭。及早识别这些家庭的目的，是及早介入，以减低受虐待的机会。为了满足这一需求，个别地方当局从盈利提供商那里委托算法风险预测系统。这一拟议项目所要解决的问题是，鉴于有关纵向数据显示，儿童保护成果的准确性较差，风险预测中的假阳性和假阴性数量高得令人无法接受，这种系统是否“适合目的”。家庭司司长最近强调了这一问题（Munby 2016）。该拟议项目通过研究以更现实的方式预测风险的新方法的可能性来解决这个问题，该方法为儿童保护系统提供了更好的支持手段，而不是必须处理可能不准确的数据。它提出了一种新的和变革性的手段，从以前的研究中整理、评估和提取一致的信息，并以一致和可靠的方式对其进行测试。潜在的可能性存在于一个新系统的范围内，该系统将算法风险预测移动到新的领域;现有系统不从这些错误中“学习”，因此技术停滞在算法预测阶段，而不是发展成为基于证据的，可靠的和响应式的人工智能（AI）。关键的研究问题/目标是：-在儿童保护的现有风险预测研究中，什么是标准化的置信限;- 开发一种新的风险计算方法，并设计其在儿童保护中的应用;-评估设计一种新的、符合GDPR的、适用于诉讼前和诉讼后儿童保护工作的风险预测人工智能模型的可能性。本研究的方法是变革性的，将传统方法和开创性方法相结合。对方法的每个阶段都进行了评估，以确定其方法和/或成果的潜在转变程度。该小组将通过创建以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的项目。其余研究所采用的创造性和新方法风险较高，但如果成功，将产生相应的高回报。在创建研究数据库后，该团队将分析其特征，规模，范围和方法，以采用一致的方法计算其功效比，创建包括优势，劣势和置信限的比较分析。这些结果将在埃格莱斯顿关于在事实调查过程中使用概率的工作中使用贝叶斯统计进行分析（埃格莱斯顿，1983年）。贝叶斯网络提供了一种新的方法来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），规划（使用决策网络）和感知（使用动态贝叶斯网络）。概率算法还可以用于过滤、预测、为数据流寻找解释，以及帮助系统分析一段时间内的过程。在这种情况下，我们将提供一个一致的措施，信心的风险因素和措施，他们的证据诚信。我们方法的这一核心变革要素将使风险预测系统的范围界定能够考虑到优势和劣势，包括确定差距，为法院提供可靠的法律的指标，以确定项目成果的适当权重。

项目成果

State powers and private rights in child protection and safeguarding assessments: the question of balance

儿童保护和保障评估中的国家权力和私人权利：平衡问题

• 发表时间: 2022
• 期刊: Child and Family Law Quarterly
• 作者: Devine L

Psy expert evidence in the family courts the potential for corpus-assisted analysis

家庭法庭上的心理学专家证据表明语料库辅助分析的潜力

• DOI: 10.21747/21833745/lanlaw/9_1a5
• 发表时间: 2022
• 期刊: Language and Law=Linguagem e Direito
• 作者: Devine L