For instance, smart analysis of historical data and real-time predictive modeling allows to predict when a calamity or epidemic will happen. Timely notifications can then help to prevent the following calamity. Artificial intelligence methods such as machine learning and deep learning make it possible to discover new new and life-saving insights that were previously not possible.
On the one hand, it is essential to be very aware of the risks of using personal data. On the other hand, by making the technologies smarter and more personal, the big data applications become indispensable to solve current societal challenges such as secure digital transformation by translating real-time data analysis from multiple sources into the optimal personal services, for the best price, best user experience and best quality.
Collaboration of various disciplines such as data science, computer science, interaction design, ethics, law, as well as of individual citizens with technology designers, public and private organizations is essential for the transparent application of smart self-learning algorithms. In addition to the importance of and the right to inspect how the automated decisions were made, the General Data Protection Regulation (GDPR) also sets requirements for the controllers responsible for the quality audit of the data sets, in particular requirements of lawfulness, fairness and transparency. Involvement of humans in the automated decision-making process remains essential to prevent the bias of algorithms (Article 22 of the GDPR).
Finally, in addition to valuable insights, personal feedback also offers other benefits for a sustainable and transparent application of big data analysis. In particular, by (a) creating more awareness about how the AI algorithms work, (b) explaining how predictive data models reason about the decisions. AI models can be iteratively improved by involving users the data analysis process and getting their feedback on the interactive representation of data.