In this workshop is focus on how to make transparency tools more usable for users. Through practical examples we talk about successes and failures in letting people to be in control of their personal data and allowing them to make sense on how their data can work in their favour.
This panel was based on usable transparency since being usable is one of the most important challenges in transparency because it deals with understanding it. One of the main questions that arises in this context is, what can we do to move from transparency awareness towards understanding? There is a need to make people in control of their data and to involve them in value creation. However, there is a complex paradox we need to balance because on the one hand, we want to convey the existing complexity in technology but that is too much for the user. On the other hand, there is a temptation to oversimplify such complexity to make people able to understand it.
Therefore, transparency is not enough. There is a need to create tools to allow users to visualize their data, to promote awareness, to give them control and create value. For example, Fing’s ‘MesInfos’ project is based on the potential of sharing data with individuals concerned by this. Users need to have access to the data that firms have on them. In fact, the self-data concept reflects how individuals become the master of their data being able to collect it, to store it in a secure way and, more importantly, to do something with it.
Another example of empowering users to manage their personal data they create is Aura, the AI of Telefónica. The goal of Aura is to create a customer relationship based on trust. AI is believed to be the way to provide interfaces to users and therefore, the way to provide a better understanding of the data by making this relationship more natural.
In fact, AI could be very useful in engaging users in all processes. Nevertheless, it might not be easy for people to understand it as the explanation of algorithms is complicated. Moreover, users often focus on understanding which data companies take as their input rather than understanding how do algorithms work.