The uncertain element 197
question of what data is being collected, the interviewees generally thought
that insurance companies are trustworthy custodians of data since they have
a long history of dealing with sensitive information. Still, they thought that
digital data has an inherent uncertainty and is prone to security breaches
(Pink, Lanzeni & Horst 2018). In a way, digital data and its movements were
seen as uncontrollable, which could lead to unwelcome surprises.
For instance, the interviewees discussed the possibility that hackers could
steal their data and use it for criminal purposes. They also speculated how
corporate acquisitions could make their data become much more widely
available than was originally intended. Moreover, people imagined how
their data could come to haunt them in unexpected contexts, such as targeted advertisements, which many customers used as a reference point to
make sense of the data’s possible movements. Targeted advertising is something that people experience in their everyday lives: their clicks, choices, and
purchases are looped back to them, sometimes creating good matches but
other times resulting in annoying and even creepy encounters (Ruckenstein
& Granroth 2020). Advertising is a concrete example of how personal data
can be used for commercial purposes, perhaps without people being aware
of it. The movements of data are just as undesirable; in the worst cases, they
violate policyholders’ sense of intimacy and self-determination.
Conclusion
Our study highlights the data ambivalence that is prevalent in customers’
relationships with behaviour-based insurance policies and the practices
those policies support. The informants were curious and interested in the
products and perceived voluntary self-tracking practices not only as acceptable but also as positive. Yet, their sense of self-determination was undermined, to varying degrees, by the fact that they were not certain of what
kind of data was being collected and to whom it was being made available.
The analysis shows that the ambivalence extends beyond the immediate
relations between people and their personal information. Uncertainties,
anxieties, and apprehensions are associated with insurance, and the data
economy at large, and the relationships embedded within these. Where will
the data travel? Will it change the insurance terms and conditions? Will it
harm me in the future?
These uncertainties undermine the policies’ trustworthiness. Although
people often regard self-tracked data as non-personal ‘background noise’
(Ajana 2020), they express concern about data movements and leakages.
Our case study highlights a generalised confusion regarding what information is being collected and by whom. In practice, privacy policies are difficult to understand – even for people working in that field – and it is clearly a
lot (too much) to ask people to familiarise themselves with details involved
in all of their data relationships. The lack of awareness and confusion exemplifies the limitations of the notice-and-consent model used, for instance, in
question of what data is being collected, the interviewees generally thought
that insurance companies are trustworthy custodians of data since they have
a long history of dealing with sensitive information. Still, they thought that
digital data has an inherent uncertainty and is prone to security breaches
(Pink, Lanzeni & Horst 2018). In a way, digital data and its movements were
seen as uncontrollable, which could lead to unwelcome surprises.
For instance, the interviewees discussed the possibility that hackers could
steal their data and use it for criminal purposes. They also speculated how
corporate acquisitions could make their data become much more widely
available than was originally intended. Moreover, people imagined how
their data could come to haunt them in unexpected contexts, such as targeted advertisements, which many customers used as a reference point to
make sense of the data’s possible movements. Targeted advertising is something that people experience in their everyday lives: their clicks, choices, and
purchases are looped back to them, sometimes creating good matches but
other times resulting in annoying and even creepy encounters (Ruckenstein
& Granroth 2020). Advertising is a concrete example of how personal data
can be used for commercial purposes, perhaps without people being aware
of it. The movements of data are just as undesirable; in the worst cases, they
violate policyholders’ sense of intimacy and self-determination.
Conclusion
Our study highlights the data ambivalence that is prevalent in customers’
relationships with behaviour-based insurance policies and the practices
those policies support. The informants were curious and interested in the
products and perceived voluntary self-tracking practices not only as acceptable but also as positive. Yet, their sense of self-determination was undermined, to varying degrees, by the fact that they were not certain of what
kind of data was being collected and to whom it was being made available.
The analysis shows that the ambivalence extends beyond the immediate
relations between people and their personal information. Uncertainties,
anxieties, and apprehensions are associated with insurance, and the data
economy at large, and the relationships embedded within these. Where will
the data travel? Will it change the insurance terms and conditions? Will it
harm me in the future?
These uncertainties undermine the policies’ trustworthiness. Although
people often regard self-tracked data as non-personal ‘background noise’
(Ajana 2020), they express concern about data movements and leakages.
Our case study highlights a generalised confusion regarding what information is being collected and by whom. In practice, privacy policies are difficult to understand – even for people working in that field – and it is clearly a
lot (too much) to ask people to familiarise themselves with details involved
in all of their data relationships. The lack of awareness and confusion exemplifies the limitations of the notice-and-consent model used, for instance, in