About us


Data science

Data science is the subject that studies formulas and strategies to translate the real world into numbers, studies how to analyse those data to find patterns and information, and uses software and technologies such as AI, to do so. Data literacy, on the other hand, can be seen as more related to ethics, safety, and privacy (using data in the best way possible for a better world) (Baston et al., 2020). Data science is a whole subject that can introduce to our children the importance of conducting research. From planning how to gather data, to collect them, analyse them with charts, and deduct information. To introduce data science to children we can use the Data Detective cycle as a framework (Leavy et al., 2012), see graphic below.

Data implications

How can AI be ethical? The impact of AI in our society is not questioned anymore, what is questioned is how this impact will be. There is a risk that AI will be overused and misused but there is also the risk that good intentions and fear could lead to underused and lack of funding to guarantee better regulations (Floridi et al., 2018). AI should be beneficial for the entire humanity, for the planet, and considered a common good.
With machine learning techniques it is possible to find patterns from big dataset, linking a specific demographic to specific habits and interests while making predictions that can influence decision-making (Kosinski, Stillwell, Graepel, 2013). This kind of privacy is called “predictive privacy” and it is not addressed by traditional privacy protection legislation (Crawford and Schultz, 2014). Despite the quantitative nature of data, numbers are not necessarily neutral and are used to translate the world we know into a language that can be manipulated with mathematics by machines. That in simple words can mean something is always left out or not considered. Datasets and how datasets are managed (algorithms) can in fact include errors and mistakes, or choices towards a part made both conscious and unconscious known as Bias (The Royal Society, 2015). Biases can have real world implications and a direct impact on people’s lives.


Crawford, K., Schultz, J., 2014. Big data and due processes: Toward a framework to redress predictive privacy harms.
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., Vayena, E., 2018. AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations.
Kosinski, M., Stillwell, D., Graepel, T., 2013. Private traits and attributes are predictable from digital records of human behavior.
The Royal Society, 2015. Unconscious bias.