Kovila Coopamootoo

Karen Elliott, Kovila Coopamootoo, Edward Curran et al.

Financial inclusion depends on providing adjusted services for citizens with disclosed vulnerabilities. At the same time, the financial industry needs to adhere to a strict regulatory framework, which is often in conflict with the desire for inclusive, adaptive, and privacy-preserving services. In this article we study how this tension impacts the deployment of privacy-sensitive technologies aimed at financial inclusion. We conduct a qualitative study with banking experts to understand their perspectives on service development for financial inclusion. We build and demonstrate a prototype solution based on open source decentralized identifiers and verifiable credentials software and report on feedback from the banking experts on this system. The technology is promising thanks to its selective disclosure of vulnerabilities to the full control of the individual. This supports GDPR requirements, but at the same time, there is a clear tension between introducing these technologies and fulfilling other regulatory requirements, particularly with respect to 'Know Your Customer.' We consider the policy implications stemming from these tensions and provide guidelines for the further design of related technologies.

Kovila P. L. Coopamootoo, Maryam Mehrnezhad, Ehsan Toreini

Online tracking is a primary concern for Internet users, yet previous research has not found a clear link between the cognitive understanding of tracking and protective actions. We postulate that protective behaviour follows affective evaluation of tracking. We conducted an online study, with N=614 participants, across the UK, Germany and France, to investigate how users feel about third-party tracking and what protective actions they take. We found that most participants' feelings about tracking were negative, described as deeply intrusive - beyond the informational sphere, including feelings of annoyance and anxiety, that predict protective actions. We also observed indications of a `privacy gender gap', where women feel more negatively about tracking, yet are less likely to take protective actions, compared to men. And less UK individuals report negative feelings and protective actions, compared to those from Germany and France. This paper contributes insights into the affective evaluation of privacy threats and how it predicts protective behaviour. It also provides a discussion on the implications of these findings for various stakeholders, make recommendations and outline avenues for future work.

Tasos Spiliotopoulos, Dave Horsfall, Magdalene Ng et al.

Vulnerable individuals have a limited ability to make reasonable financial decisions and choices and, thus, the level of care that is appropriate to be provided to them by financial institutions may be different from that required for other consumers. Therefore, identifying vulnerability is of central importance for the design and effective provision of financial services and products. However, validating the information that customers share and respecting their privacy are both particularly important in finance and this poses a challenge for identifying and caring for vulnerable populations. This position paper examines the potential of the combination of two emerging technologies, Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs), for the identification of vulnerable consumers in finance in an efficient and privacy-preserving manner.

Ehsan Toreini, Mhairi Aitken, Kovila P. L. Coopamootoo et al.

Concerns about the societal impact of AI-based services and systems has encouraged governments and other organisations around the world to propose AI policy frameworks to address fairness, accountability, transparency and related topics. To achieve the objectives of these frameworks, the data and software engineers who build machine-learning systems require knowledge about a variety of relevant supporting tools and techniques. In this paper we provide an overview of technologies that support building trustworthy machine learning systems, i.e., systems whose properties justify that people place trust in them. We argue that four categories of system properties are instrumental in achieving the policy objectives, namely fairness, explainability, auditability and safety & security (FEAS). We discuss how these properties need to be considered across all stages of the machine learning life cycle, from data collection through run-time model inference. As a consequence, we survey in this paper the main technologies with respect to all four of the FEAS properties, for data-centric as well as model-centric stages of the machine learning system life cycle. We conclude with an identification of open research problems, with a particular focus on the connection between trustworthy machine learning technologies and their implications for individuals and society.

Magdalene Ng, Kovila P. L. Coopamootoo, Ehsan Toreini et al.

FinBots are chatbots built on automated decision technology, aimed to facilitate accessible banking and to support customers in making financial decisions. Chatbots are increasing in prevalence, sometimes even equipped to mimic human social rules, expectations and norms, decreasing the necessity for human-to-human interaction. As banks and financial advisory platforms move towards creating bots that enhance the current state of consumer trust and adoption rates, we investigated the effects of chatbot vignettes with and without socio-emotional features on intention to use the chatbot for financial support purposes. We conducted a between-subject online experiment with N = 410 participants. Participants in the control group were provided with a vignette describing a secure and reliable chatbot called XRO23, whereas participants in the experimental group were presented with a vignette describing a secure and reliable chatbot that is more human-like and named Emma. We found that Vignette Emma did not increase participants' trust levels nor lowered their privacy concerns even though it increased perception of social presence. However, we found that intention to use the presented chatbot for financial support was positively influenced by perceived humanness and trust in the bot. Participants were also more willing to share financially-sensitive information such as account number, sort code and payments information to XRO23 compared to Emma - revealing a preference for a technical and mechanical FinBot in information sharing. Overall, this research contributes to our understanding of the intention to use chatbots with different features as financial technology, in particular that socio-emotional support may not be favoured when designed independently of financial function.

Kovila P. L. Coopamootoo

While it is often claimed that users are empowered via online technologies, there is also a general feeling of privacy dis-empowerment. We investigate the perception of privacy and sharing empowerment online, as well as the use of privacy technologies, via a cross-national online study with N=907 participants. We find that perception of privacy empowerment differs from that of sharing across dimensions of meaningfulness, competence and choice. We find similarities and differences in privacy method preference between the US, UK and Germany. We also find that non-technology methods of privacy protection are among the most preferred methods, while more advanced and standalone privacy technologies are least preferred.. By mapping the perception of privacy dis-empowerment into patterns of privacy behavior online, and clarifying the similarities and distinctions in privacy technology use, this paper provides an important foundation for future research and the design of privacy technologies. The findings may be used across disciplines to develop more user-centric privacy technologies, that support and enable the user.

Ehsan Toreini, Mhairi Aitken, Kovila Coopamootoo et al.

To build AI-based systems that users and the public can justifiably trust one needs to understand how machine learning technologies impact trust put in these services. To guide technology developments, this paper provides a systematic approach to relate social science concepts of trust with the technologies used in AI-based services and products. We conceive trust as discussed in the ABI (Ability, Benevolence, Integrity) framework and use a recently proposed mapping of ABI on qualities of technologies. We consider four categories of machine learning technologies, namely these for Fairness, Explainability, Auditability and Safety (FEAS) and discuss if and how these possess the required qualities. Trust can be impacted throughout the life cycle of AI-based systems, and we introduce the concept of Chain of Trust to discuss technological needs for trust in different stages of the life cycle. FEAS has obvious relations with known frameworks and therefore we relate FEAS to a variety of international Principled AI policy and technology frameworks that have emerged in recent years.