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

机器学习算法(ML)正被用于自动化各种任务，从信贷贷款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和2008年金融危机使风险评估变得更加重要，在该领域实施最大似然算法和使用替代数据源的情况有所增加。这些实现旨在创建更准确和高效的方法，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，人们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式(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他们的消费者隐私。保证这一点的一种方法是实现一种不同的私有机器学习模型。差异隐私是一种最先进的隐私增强技术，它允许用户在不危及个人隐私的情况下收集聚合信息，但这是以牺牲隐私准确性为代价的。一般研究问题：在信用风险评估中潜在地实施差异隐私对消费者信贷申请有什么影响？我对这一研究问题的方法考虑了所有涉及的利益相关者，同时仍然以用户/消费者/人为中心的方法，因为他们是受影响最大、影响力较小的利益相关者。为了开始回答上述问题，设计了以下研究，每个研究都有自己的研究问题，以开始获得关于该行业、其影响和技术的知识。在这项研究中，我们的目的是了解参与者在申请贷款时对他们的经历的感受，以及他们对贷款过程的自动化、数据共享和公平性的态度。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的数据收集，再到客户服务的自动化，以及应用程序本身(例如简短的在线表格)。我们的研究重点是英国的消费信贷行业。这一贡献不同于现有的关于算法轰动效应的文献，因为在这一过程中，用户缺乏代理。它还解决了用户对技术在金融服务中的作用缺乏看法的问题。英国消费信贷行业利益相关者咨询这项基于访谈的研究针对在英国消费信贷行业工作或曾经在英国消费信贷行业工作过的参与者，旨在基于参与者的数据对消费信贷行业的运作进行非正式知识。访谈分为两部分，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角色和内部运作，以及他们之间的互动。以及了解行业中新技术的实施过程：哪些利益相关者参与，以及如何参与？利益相关者之间的权力差异是什么？这对技术实施有何影响？有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的发展方向，并收集利益相关者对差异隐私及其在行业中实施的潜在影响的态度。基于不同私有决策树的模型：探索性探讨基于不同私有决策树的模型研究是一种探索性的研究，包括对三个信用-商业银行的不同DP模型的实现。

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-