The main purpose of DTL is to support the development of software on data transparency & privacy. With this aim DTL launched this international funding opportunity to enable startups, companies, entrepreneurs, students, universities and research centers to develop apps, tools, libraries and other forms of software that provide privacy and transparency to users on how their data is being used when they connect to online services: which data is collected, who is collecting that data and how it is used. The aim of this call was to give economic and technical support to develop apps, tools, libraries and other forms of software that explore topics aligned with Data Transparency Lab’s mission.
We received around 50 projects from 17 countries and for the first time participants from Argentina, Colombia, Congo, Peru & Turkey have applied to our call. The awarded projects were announced at DTL Conference on November 15th. Meet the awarded projects below:
Facebook Tracking Exposed
Grant amount: €20,000
Description: Personalization algorithms are tools to customize information, determining its importance for a specific user. On the basis of users’ identity and interactions, specific bits of information are preferred while other are hidden from her view. By deploying this filtering mechanism, Facebook ensure that each user can have a meaningful experience of the service, instead of dealing with an unorganized information flow. Personalization algorithms have to be judged positively in its potential to engage with basic human need in the information era: reduce information overload, increase its scope and usability, and support users autonomy of making informed choices. However, while algorithms facilitate the managing of information, new researches have shown evidences that this benefit is largely balanced by the social and individual negative effect of filtering. Our vision is to increase transparency behind personalization algorithms, so that people can have more effective control of their online Facebook experience and more awareness of the information to which they are exposed.
Grant amount: €10,000
Description: MalditaApp is a tool that alerts of disinformation when surfing the web on a smartphone. The open source project consists of an Android application, compatible with API level > 21, that will warn users when they open a webpage that has been debunked by Maldita.es’s fact-checking project Maldito Bulo. The App will connect to our database endpoints in order to get the required information and then check locally if the website or the information itself has been previously debunked and, in that case, will show a notification to the user. Creation and distribution of disinformation is very easy nowadays. This, together with isolated consumption through social media, has made the ecosystem more dangerous. Furthermore, smartphone news consumption has risen in Spain in the last year (Reuters Institute Report). We believe that there’s a need for a tool that alerts you when you access a disinformation site in order to enable citizens to build informed opinions and make informed decisions. In addition, the app would also offer the capability of searching through our debunks using two different approaches: our users can search by keywords, for what we need to improve our search engine with an Elasticsearch implementation on our server side; and they will be able to search by images. It will allow the user to select an image from the device’s gallery and combining different techniques (three different types of image hashes and OCR) will detect if a similar image has been already debunked or an audio which will be transcribed and used as a base to search by keyword.
Grant amount: €20,000
Description: The general goal of our activities is to make personal data rights practical and useful, and assist other organisations in doing so. We think collaboratively building a registry of data processing operations (made available as open data) would be transformative towards our and their goals. The main idea would be to replicate the process around OpenStreetMap, with the goal of flexibly mapping data flows, and building some smart clients navigating the map. This means we would map nodes (controlling data), flows of data between them, more complex relationships between all those, and tag each of those entities in such a way that the tags are simultaneously flexible yet provide structure if desired. We want to replicate the practices of OpenStreetMap, where this is done through key-value tags, where the keys and values can themselves have an additional hierarchy, but don’t have to. This is good practice as the flexibility eases newcomers into contributing. On the mapping side, we expect contributions from experts in many different lines of work: activists, academics, entrepreneurs, regulators, legal experts, technologists, communities built specifically around personal data empowerment. We therefore expect tags and contributions along many different dimensions: some will cover legal aspects, others will cover semantics of the data held, others will cover formats, technology-driven audits (trackers on websites/apps), etc. There are some separate efforts in those different communities to do this mapping, so we will focus on the tooling that will maintain the mapping effort anti-rival [https://en.wikipedia.org/wiki/Anti-rival_good]. This means focusing on tools: that integrate data from disparate sources; that make it easier and more effective to contribute; that make this map actionable as fast as possible.