Personal Data: Analytics and Management
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Item“Healthy surveillance”: Designing a concept for privacy-preserving mask recognition AI in the age of pandemics( 2021-01-05)The obligation to wear masks in times of pandemics reduces the risk of spreading viruses. In case of the COVID-19 pandemic in 2020, many governments recommended or even obligated their citizens to wear masks as an effective countermeasure. In order to continuously monitor the compliance of this policy measure in public spaces like restaurants or tram stations by public authorities, one scalable and automatable option depicts the application of surveillance systems, i.e., CCTV. However, large-scale monitoring of mask recognition does not only require a well-performing Artificial Intelligence, but also ensure that no privacy issues are introduced, as surveillance is a deterrent for citizens and regulations like General Data Protection Regulation (GDPR) demand strict regulations of such personal data. In this work, we show how a privacy-preserving mask recognition artifact could look like, demonstrate different options for implementation and evaluate performances. Our conceptual deep-learning based Artificial Intelligence is able to achieve detection performances between 95% and 99% in a privacy-friendly setting. On that basis, we elaborate on the trade-off between the level of privacy preservation and Artificial Intelligence performance, i.e. the “price of privacy”.
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ItemData are in the Eye of the Beholder: Co-creating the Value of Personal Data( 2021-01-05)The value of personal data has traditionally been understood in economic terms, but recent scholarship casts the value of data as multi-faceted, dynamic, emergent and co-created by stakeholders. The dynamics of the co-creation of value with personal data lacks empirical study. We conduct a case study of the development of a personalised e-book and find different perceptions of the value of personal data exist from the firm, intermediary and customer perspective: means to an end, medium of exchange and net benefit. The different data perspectives highlight ontological differences in the perception of what data are. This creates epistemological tension and different expectations of the data characteristics embedded in the process of value co-creation. The findings contribute to the growing data-in-practice literature, showing how different epistemological stances can create opposing expectations of what data should be, leading to ontological, policy and managerial tensions.
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ItemCircular Insurance: customer-centric, data-driven services for the Circular Economy( 2021-01-05)Advances in digital technology are driving the digital transformation of many sectors facilitating new business models, changes to business processes and the emergence of technology-driven start-ups. It is clear that technology is important in facilitating a servitisation business model. However, customers are increasingly identified as the ones driving innovation and, therefore, the digital transformation of an organisation. There is also a push for all sectors to play their role in moving to a more sustainable system by embedding Circular Economy principles into their organisations. This conceptual paper explores the interactions between digital transformation, servitisation and the Circular Economy and how these might transform the insurance sector, leading to the proposal of a new finance insurance ecosystem, Circular Insurance. The implications for the future of the insurance market are explored highlighting areas of future research and the beginnings of a possible research agenda.
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