A Sociotechnical Evaluation of Differentially Private Risk Assessment Models in the Consumer Credit

消费信贷差异化私人风险评估模型的社会技术评价

• 批准号: 2278911
• 金额: --
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2278911
• 关键词: Sociotechnical; Evaluation; Differentially; Private; Risk

Machine learning algorithms (ML) are being adopted to automate a variety of tasks, from credit loan decisions to health diagnostics, among many others. More recently in the area of credit risk assessment, due to the advances in Machine Learning and a bigger importance of risk assessment due to the 2008 financial crisis, there has been a rise in the implementation of ML algorithms and use of alternative data sources in the area. These implementations are meant to create more accurate and efficient methods, and by using different data sources they are also able to score people that previously were excluded form the credit industry. However, as these technologies become ever more complex there has been a demand for more transparency regarding models. If companies are required to share their models (either with the regulator or wider public) they still have a duty to protect their consumers privacy. One way to guarantee this is to implement a differentially private machine learning model. Differential Privacy is a state-of-the-art Privacy Enhancing Technology which allows one to gather aggregated information without risking individual's privacy, however it comes at the cost of a privacy accuracy trade-off.General Research Question: What is the impact of the potential implementation of Differential Privacy in Credit Risk assessment on consumer credit applications? My approach to this research question takes in consideration all stakeholders involved while still having a user/consumer/human centred approach, as these are the most affected and less powerful stakeholders. In order to start answering the question above the following studies were design each with research questions of their own to start gaining knowledge on the industry, its impact and the technology. Attitudes and Experiences with Loan Applications In this study we aim to understand participant's sensemaking of their experiences when applying for loans, as well as their attitudes regarding automation, data sharing and fairness of the process. In this context automation encompasses processes from the statistical and ML methods used for decision making, to data gathering making use of different information systems, to the automation of customer service, as well as application process itself (for example short online forms). Our study focuses specifically on the UK consumer credit industry. This contribution differs from existing literature regarding algorithmic sensemaking as there is a lack of agency on the part of the user in the process. It also addresses the lack of users' perspective on the role of technology in financial services.UK Consumer Credit Industry Stakeholder Consultation This interview-based study with participants who work or have worked within or with the UK Consumer Credit Industry aimed to ground informal knowledge on the workings of the consumer credit industry on participant's data. The interview was divided into two parts, the first aimed to gain better understanding of the Consumer Credit Ecosystem, including gaining a better awareness and understanding of the role and inner workings of the different stakeholders, and interactions between them. As well as understanding the process of new tech implementation in the industry: which stakeholders are involved and how? What are the power differences between stakeholders and how does this affect tech implementation? Which external factors are at play? The second part of the interview was aimed at understanding the importance and current practices regarding privacy in the industry as well as future directions and gather Stakeholders attitudes towards Differential Privacy and potential impacts of its implementation in the industry. Differentially Private Decision Tree based Models: exploratory inquiry The Differentially Private Decision Tree based Model study is of an exploratory nature and consists of the implementation of different DP models on three credit-

机器学习算法（ML）正在被用于自动化各种任务，从信贷决策到健康诊断等等。最近在信用风险评估领域，由于机器学习的进步以及2008年金融危机导致的风险评估的重要性，ML算法的实施和该领域替代数据源的使用有所增加。这些实现旨在创建更准确和有效的方法，通过使用不同的数据源，它们还能够对以前被排除在信贷行业之外的人进行评分。然而，随着这些技术变得越来越复杂，对模型的透明度提出了更高的要求。如果公司被要求分享他们的模型（无论是与监管机构还是更广泛的公众），他们仍然有责任保护消费者的隐私。保证这一点的一种方法是实现差异化的私有机器学习模型。差分隐私是一个国家的最先进的隐私增强技术，它允许一个收集汇总信息，而不会冒着个人的隐私，但它是在一个隐私的准确性trade-off.General研究问题的成本：什么是潜在的实施差分隐私在信用风险评估消费信贷应用的影响？我对这个研究问题的方法考虑到了所有相关的利益相关者，同时仍然采用以用户/消费者/人为中心的方法，因为这些是受影响最大和影响力较小的利益相关者。为了开始回答上述问题，我们设计了以下研究，每个研究都有自己的研究问题，以开始了解行业，其影响和技术。贷款申请的态度和经验在这项研究中，我们旨在了解参与者在申请贷款时对自己经历的理解，以及他们对自动化、数据共享和过程公平性的态度。在这种情况下，自动化包括从用于决策的统计和ML方法到利用不同信息系统收集数据，再到客户服务自动化以及应用程序本身（例如简短的在线表单）的过程。我们的研究主要集中在英国的消费信贷行业。这一贡献不同于现有的文献中关于算法的意义建构，因为在这个过程中缺乏用户的代理。英国消费信贷行业利益相关者咨询这项基于访谈的研究，参与者在英国消费信贷行业内或与英国消费信贷行业合作，目的是根据参与者的数据，对消费信贷行业的运作进行非正式的了解。访谈分为两个部分，第一部分旨在更好地了解消费信贷生态系统，包括更好地认识和了解不同利益相关者的作用和内部运作，以及他们之间的互动。以及了解行业中新技术实施的过程：哪些利益相关者参与其中，以及如何参与？利益相关者之间的权力差异是什么？这如何影响技术实施？哪些外部因素在起作用？访谈的第二部分旨在了解行业中有关隐私的重要性和当前做法以及未来方向，并收集利益相关者对差异隐私的态度及其在行业中实施的潜在影响。基于差异化私有决策树的模型：探索性研究基于差异化私有决策树的模型研究具有探索性，包括在三个信用上实现不同的DP模型，