By enabling people to share their apartments, cars and parking spaces, the sharing economy can be a source of economic empowerment. At the same time, users take part in an invisible market for their data.

Data about how people use services is valuable because it can help companies run their business more efficiently. Companies can also sell the data they collect about their users to other third parties interested in marketing their products or services to the same groups. For example, a cleaning company may be interested in advertising its services to Airbnb hosts.

At the same time, the value of data is inherently unpredictable. It is impossible for companies to know how much customers will pay for the data in the future. Therefore, users and companies can enter a relationship of unequal exchange, where users hand over potentially valuable data with no opportunity for adequate compensation.  Below we suggest four different scenarios for future data value.

Four Scenarios for the Future Value of Data

1.      Informed Consent

In an ideal but unrealistic scenario, companies and users both know the future value of data. In such perfect conditions, the user would be free to either hand over their data or not depending on the conditions and compensation offered by the company.

2.      Bets on the Future

In the most likely future scenario, companies and users are both unaware of the future value of data. Companies will want to collect as much data as possible, thus making bets on the future value of this data. Depending on what happens, they may gain money or lose money.

This is likely to create a situation where the companies who invest heavily in data collection and data processing have much more power than users, who may not even understand what data is collected, how it is collected and how it is used.

3.      Alternative Use

This is a situation where the users know the future value of their data, and the companies do not. This scenario relies on users challenging the companies about how they collect and use data. Users can do this through working out how a system works (reverse engineering) or developing alternative services  to data-hungry companies (competitors to Airbnb or Uber, for example).

We argue that alternative data use can create value for both parties. Users would be free to experiment and innovate, and companies would have to compete to keep them.

Inviting as this scenario may be, it is unlikely in the current world where companies work actively to counter user creativity and attempts at gaining ownership to and control over their own data.

4.      Finders Keepers

This scenario most accurately reflects the current situation, where companies have more awareness about the future value of data than users. This is because companies, especially platform companies, monopolize the collection, use and sales of it. They use this power to increase the collection of data which is likely to have a high future value, and to maintain their monopoly by countering user creativity and user attempts at gaining ownership and control over their own data.

How to empower users of platform companies?

Uber would be worthless without drivers and Airbnb would be worthless without hosts. We therefore argue that there is a strong case for co-creation on these services, recognizing both the contributions of the companies and of the users.

Platform companies may be good test cases for a fairer distribution of data value. One where platform companies act more cooperatively and enable innovation through data sharing. This is in stark contrast to current developments, where almost all the power lies with the companies.

Policy makers, users, the media, educational institutions and platform companies themselves need to work together to instead allow for the more user-friendly scenarios – either alternative use or informed consent.

 

Based on Newlands, G., Lutz, C. & Fieseler, C. (2019). Trading on the Unknown: Scenarios for the Future Value of Data . Sharing Economy Markets and Human Rights. The Law & Ethics of Human Rights, 13(1), pp. 97-114. Retrieved 30 Jan. 2020, from doi:10.1515/lehr-2019-0004

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