AI-generated summary
Data science is an interdisciplinary field combining mathematics, statistics, IT, business, and corporate knowledge to analyze large data sets. Nuria Oliver highlights a specialized area within data science called computational social science, which uses aggregated behavioral data to test social science theories. Over the past decade, Oliver has focused on “data science for social good,” applying data analysis to improve decision-making in public health, financial inclusion, climate change, and emergency relief efforts.
The field relies heavily on the vast amount of data generated annually—more than the previous 5,000 years combined—and the ability to interpret this data effectively is crucial. The European data economy is projected to be worth over 700 billion euros soon, contingent on supportive policies and investments in ICT. However, having data alone is insufficient; expertise in data-driven sciences, especially machine learning, is essential to avoid turning data into unusable digital waste.
Experts identify six critical areas for data science improvement: privacy violations, biased discrimination, informational asymmetry, opacity, veracity, and ethics. Future data processing must employ FATEN algorithms, which emphasize fairness, autonomy, trust, education, and minimizing harm. These principles aim to ensure non-discrimination, human accountability, transparency, ethical benefits, and privacy preservation in data science applications.
Data science is a set of tools that emerged out of the intersection of math and statistics, IT, business management, corporate knowledge and other relevant sectors.
Data science is a set of tools that emerged out of the intersection of math and statistics, IT, business management, corporate knowledge and other relevant sectors.
Nuria Oliver says a new field within data science, computational social science, leverages large-scale, aggregated behavioral data in order to verify social science theories. For the last ten years, Oliver has also been working in an area of computational social science: data science for social good.
The goal of this area is to leverage the analysis and processing of massive data sets in order to make better decisions in fields like public health, financial inclusion, climate change or emergency relief during natural disasters.
Data science needs data. Every year, we generate more data than in the previous 5,000 years combined, and the ability to interpret it is increasingly becoming an incredibly valuable asset in today’s economy. According to a European Commission report, the European data economy will be worth over 700 billion next year, as long as favorable policy and legislation is implemented in time and investment in ICTs is promoted.
Data science also needs science. It’s good to have the data, but if you don’t know how to use it, the data becomes digital waste. We need to know how to interpret data–large volumes of unstructured data– which are invisible and incomprehensible without science and its disciplines, particularly data-driven machine learning.
According to our FTF experts, it must improve in 6 main areas:
1. Computational-privacy violation
2. Biased social exclusion or discrimination
3. Asymmetry in informational skills (there’s plenty of data, but most of it is in the hands of private actors)
4. Opacity and lack of transparency
5. Veracity
6. Ethics
FATEN algorithms
In addition, data science will, in the future, have to process data using FATEN algorithms, which take into account: – F for fairness, focusing on non-discrimination and cooperation – A for autonomy, accountability y augmented, referring to the need to preserve human sovereignty, clear accountability and models that increase –not replace– human intelligence- T for trust and transparency– E for education and beneficence– N for non-maleficence, minimizing the negative aspects and ensuring a minimum level of reliability, reproducibility and caution, always preserving people’s privacy.
Director Data Scientist at Data Pop Alliance
