As we already know, failure to comply with GDPR regulation it can involve fines of up to 20 million euros or 4% of the company’s global turnover. Until September 2021, the total amount of fines amounts to more than 1,000 million Euros. amazon It has been the company that has been fined the most, followed by WhatsApp and Google, according to a study by EAE Business School.
“The fact that companies have information about users that they did not have before, and tools such as Big Data to exploit it, opens the door to consider where the user is located in this situation. Determining if Big Data is beneficial for users is a very complex task. In some cases it allows the existence of certain useful services but, on the other hand, misuse of big data can lead to abuse of user rights”, warns Pau Sabaté, co-author of the EAE Business School study.
In this sense, the General Data Protection Regulation (RGDP) (GDPREU) regulates the protection of people with respect to their personal data and the circulation of these data throughout the European territory, which entered into force in 2016 and it has been applied since 2018, as stated in the EAE study. This regulation ensures the rights of users in the collection of data, the introduction of rights such as the right to be forgotten, the limitation of their treatment and data, the obligation to carry out a risk analysis or the consent of the users, among others. others.
The challenges of Big Data
The irruption of the digital ecosystem has exponentially generated an amount of data that seems to have no limit or stop its creation. In the last five years, the amount of digital data created or replicated around the world has multiplied by four, and it is estimated that by 2025 this figure will at least double, as stated in the EAE Business School report.
The energy cost of Big Data is another topic to address. The electricity consumption of all the data centers together already represents 1% of the global demand for electricity.
Another field where this technology is more immature is in understanding and managing the impact it has on a social level. For example, as the study points out, recommendation systems can create information bubbles where people only receive information from the part of society close to their ideology, as a self-confirmation bias. Recommender systems are also likely to be used to spread fake news.
Another risk may be the granting of a mortgage. By launching the predictive model, it can give less financing possibilities to a certain social profile, and therefore of development, creating a possible social inequality.
“These types of problems, related to ethics and inequality, correspond to the area of fairness within machine learning, and are difficult to solve, since they not only require technical knowledge, but also social knowledge”explains the co-author of the study and professor at EAE Business School, Aleix Ruiz de Villa.
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