DATA PROTECTION AND DATA SAFETY

According to the DETECT Critical Digital Literacies framework, data protection in simple terms covers who has the right to access data about individuals and how they store and use it. Data protection is covered by laws such as the General Data Protection Regulations (GDPR) at a pan-European level. However, the key critical aspects are not just how organisations structure themselves to protect our data in line with legislation, but how we as citizens develop an understanding of the data we are sharing, and how it is being used - how we develop our own sense of agency regarding our data and own its use. Concerns related to the use of data can impact on the safety of citizens not just to result in a financial penalty, but also personal safety issues about the sharing of location data etc. Therefore at the school level the key issues to engage with are developing an understanding of the data that each individual student shares, understanding who they share it with, the risks associated with this, and understanding the structures that are there to help us protect our data, including proactive decisions about not using particular applications or websites.

[Recommended Education Level: all] - [DETECT FRAMEWORK AREA: Data Protection and Safety]

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OPEN DATA TO DEVELOP CRITICAL CITIZENS' DATA LITERACIES

According to the Open Data Handbook definition, "Open data is data that can be freely used, re-used and redistributed by anyone - subject only, at most, to the requirement to attribute and share-alike". With the digitalization of services and research data collection in the public space, opening digital data has become a crucial side of open public knowledge. There are thousands of public government and research datasets that are being shared on the web. Nevertheless, their usage requires skills and knowledge to critically read, extract, process, elaborate and integrate data into relevant activities such as civic monitoring, social and political activism, or participation in responsible research and innovation. The unleash the power of open data, there is a need to overcome the "data divide". An excellent approach is to understanding open data, discovering and engaging with open datasets and platforms, extracting specific data and using them in forms that are relevant for the social contexts where the learners live. Moreover, while advanced forms of open data usage relate to professional and academic scenarios of practice, there are basic activities that promote data literacy at school.

[Recommended Education Level: lower and upper secondary school] - [DETECT FRAMEWORK AREA: The use of Big/Open Data]

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DATA JUSTICE: EXPLORING THE DARK SIDE OF DATA IN SOCIETY

Digital data, as a sort of DNA of information, are addressing contemporary social practices. The most enthusiastic discourses on data abundance have emphasized the opportunity to generate new business models, new professional landscapes connected to the science of data, open practices in science and the public space. But more recently, the possibility of data capture and articulation throughout several algorithms as drivers of more economical and objective social practices has been called into question. In fact, many forms of invisibility or overrepresentation, as well as algorithmic bias have been uncovered in several cases. In her book "Data Feminism" (2020) Caterine D'Ignazio explores several injustices in the way some collectives are represented (particularly woman) in data science. Several cases of injustice through algorithmic bias have been reported by Cathy O'Neil in her book Weapons of Math Distruction* (2016) or by Virginia Eubanks in her book "Automating Inequality* (2018). In fact, Linnet Taylor defines data justice as fairness in the way people are made visible, represented and treated as a result of their production of digital data. Moreover, she points out that data justice is necessary to determine ethical paths through a datafying world. The educators can engage with this perspective, working.

*Book reviews

[Recommended Education Level: upper secondary] - [DETECT FRAMEWORK AREA: Data Analytics / Data visualization]

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🎯Go to the Further Learning section if you want to learn more.