I am Ramón Sangüesa, the coordinator of the Data Transparency Lab and the Data Transparency Conference

How would you define Transparency?
I always define transparency the same way, first you have to take into account that we only speak about personal data transparency. I mean, transparency is even a broader concept that is used to in many other contexts, so we just focus on personal data transparency, which basically is to understand what’s happening with the data that describes you, which is another thing to discuss, right? But basically, that data that describes you have, let’s say, the right to understand who is using it and under which kind of context and conditions and how this affects you, to put it the shortest way possible. So, what the Data Transparency Lab really does is just to create technologies that allow you to actually notice.

Which have been the last trends at DTL Conference?
Well there’s something that we are seeing in this edition, DTL 17, which is basically people are starting to show applications that have to do for example with the Internet of Things, because it’s not just that we relate to how to call it the InfoSphere, the cloud or whatever through just our laptop, so just our smartphones. More and more there are many devices out there that are capturing our data, our doings and goings, and that’s what we’re seen this year more discussion and more applications that deal with how to reveal what’s happening with the data when you’re out there and this device is capturing. So, this one thing, but the other thing I am very proud of, is that we are kind of merging the ways of working and doing research with other communities that are not just the technological ones. For example, we had a very good panel on design. How to apply design methodologies, to actually know better the user, how to create a type of tools that different types of users may need and may understand. One of the things that also has come up these days is the importance of being understandable, so to speak. So there has been a kind of shift towards not just saying which data has been used, but to understand the way that is being used. And this is not a simple thing. So, there are ways of doing reverse engineering to understand what’s happening, but then you have to make an effort to communicate that to again different types of users and different types of users that have different technical backgrounds and interests. And so, that’s also a very interesting area that is now coming up.

Some of the conversations that took place in DTL 3 years ago are now giving some kind of different fruits in the sense that technologists are coming up with different responses to new questions that couldn’t probably posed by themselves, if they didn’t have a first talk with policy makers, designers or plane people.

 

¿How is DTL helping the research community?
Data Transparency Lab is helping just by giving money. Almost everyone likes to have money to do the thing that they like the most and researchers do like to actually perform research and we are just funding them. Every year between five to six, we would like to have even more grants, that actually develop the new technologies that’s the main way that we help achieving this kind of goals. And the other one is connecting people, that sounds kind of Nokia, connecting people of different backgrounds and communities to start different types of conversations and we know that some of the things that started three years ago in these conversations that took place in DTL are now giving some kind of different fruits in the sense that technologists are coming up with different responses to new questions that couldn’t probably posed by themselves, if they didn’t have a first talk with policy makers, designers or plane people. The other thing that I am happy that we are starting to have as participants, as attendants, people that in principle have no technical qualification, no special background, but they are interested in the problem of privacy and data transparency which this is in the end who we are working for.

DTL2018

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