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

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-

We will collaborate with over 40 partners drawn from across FMCG and Food; Creative Industries; Health and Wellbeing; Smart Mobility; Finance; Enabling technologies; and Policy, Law and Society. These will benefit from engagement with our CDT through the following established mechanisms:

- Training multi-disciplinary leaders. Our partners will benefit from being able to recruit highly skilled individuals who are able to work across technologies, methods and sectors and in multi-disciplinary teams. We will deliver at least 65 skilled PhD graduates into the Digital Economy.

- Internships. Each Horizon student undertakes at least one industry internship or exchange at an external partner. These internships have a benefit to the student in developing their appreciation of the relevance of their PhD to the external societal and industrial context, and have a benefit to the external partner through engagement with our students and their multidisciplinary skill sets combined with an ability to help innovate new ideas and approaches with minimal long-term risk. Internships are a compulsory part of our programme, taking place in the summer of the first year. We will deliver at least 65 internships with partners.

- Industry-led challenge projects. Each student participates in an industry-led group project in their second year. Our partners benefit from being able to commission focused research projects to help them answer a challenge that they could not normally fund from their core resources. We will deliver at least 15 such projects (3 a year) throughout the lifetime of the CDT.

- Industry-relevant PhD projects. Each student delivers a PhD thesis project in collaboration with at least one external partner who benefits from being able to engage in longer-term and deeper research that they would not normally be able to undertake, especially for those who do not have their own dedicated R&D labs. We will deliver at least 65 such PhDs over the lifetime of this CDT renewal.

- Public engagement. All students receive training in public engagement and learn to communicate their findings through press releases, media coverage.

This proposal introduces two new impact channels in order to further the impact of our students' work and help widen our network of partners.

- The Horizon Impact Fund. Final year students can apply for support to undertake short impact projects. This benefits industry partners, public and third sector partners, academic partners and the wider public benefit from targeted activities that deepen the impact of individual students' PhD work. This will support activities such as developing plans for spin-outs and commercialization; establishing an IP position; preparing and documenting open-source software or datasets; and developing tourable public experiences.

- ORBIT as an impact partner for RRI. Students will embed findings and methods for Responsible Research Innovation into the national training programme that is delivered by ORBIT, the Observatory for Responsible Research and Innovation in ICT (www.orbit-rri.org). Through our direct partnership with ORBIT all Horizon CDT students will be encouraged to write up their experience of RRI as contributions to ORBIT so as to ensure that their PhD research will not only gain visibility but also inform future RRI training and education. PhD projects that are predominantly in the area of RRI are expected to contribute to new training modules, online tools or other ORBIT services.