PARTNER FEATURE: It would be an understatement to say that the era of Big Data is upon us. While a lot of the data is not necessarily human-generated, a fraction of this data is created everyday each of us through our interactions in the physical and digital worlds, enabled by the ubiquity of digital services, devices and sensors

It is the symbiosis between the digital, the physical and the biological worlds being experienced today that has led to the Fourth Industrial Revolution. This is symbolized by breakthroughs in multiple fields, including robotics, artificial intelligence, nanotechnology, quantum computing, biotechnology, fifth-generation wireless technologies (5G), fully autonomous vehicles, etc. These technologies are certainly doing their bit to disrupt almost every industry globally. They have the potential to narrow the digital divide between emerging and the developed countries, which is what motivates my work.

Data is a key asset of this Fourth Industrial Revolution and has led to the emergence of what is called the Data Economy, which not only generates hundreds of thousands of jobs but also significant economic contributions both in terms of savings and economic growth . However, data in itself is just a window to a complex reality. As such, it is not only of large volumes but also extremely diverse, dynamic and complex. In fact, data would simply be “digital garbage” if we are unable to make sense of it. Hence, we need to rely of sophisticated data analytics techniques including statistical machine learning in order to draw useful insights, learn and make better decisions with the data.

An element of this abundance of data is the unprecedented availability of human behavioral data –a lot of it thanks to the ubiquity of mobile phones– which has led to the emergence of a new discipline called Computational Social Sciences. Such data can be used to train algorithms, allowing researchers, companies, governments and other public-sector stakeholders to tackle complex problems. Data-driven machine learning algorithms applied on different types of Big Data help us shed light on how global challenges, such as climate change, infectious diseases, socio-economic development, transportation, urban planning and energy. In fact, there is a world movement studying and working on how we can contribute to achieving the 17 Sustainable Development Goals through the analysis of Big Data. This has been an area of research for me for almost 10 years.

Our efforts have highlighted that pseudonymized large-scale aggregated mobile network data is invaluable on several counts. Through the analysis of the data we can infer large-scale patterns of human mobility, characterize human networks and compute accurate estimates of population counts, in a fully privacy-preserving manner. These variables (mobility, networks and population counts) are of vital importance in multiple use cases, including urban planning, public health and natural disasters and emergencies. Moreover, population dynamics are also vital to comprehend the socio-economic development of a region, model energy consumption or to automatically detect crime hotspots in a city.

Ample opportunities are also offered within the financial sector, such as personalized data-driven services; new ways to assess credit worthiness, risk and fraud through the analysis of the data –as I did in the MobiScore project a few years ago– and new services that foster financial inclusion, such as mobile money like M-PESA. Equally important and interesting are new methods to infer and predict socio-economic status, prosperity, poverty and resilience through the analysis of the data. The potential for having positive impact in the lives of millions of people is huge.

However, there are numerous technical, legal, financial and ethical challenges that we will need to overcome before we can truly leverage this data for positive social impact. From a technical perspective, challenges include the need for real-time modeling and prediction, the power of combining data from different sources in an effective and privacy-preserving way and the development of models of causality. From a legal perspective, we need to ensure that the legal frameworks are updated to consider using data for social good purposes. From a financial perspective, we should identify suitable business models to ensure that the projects are financially sustainable over time. Finally, from an ethical perspective, we need to ensure that data-driven algorithmic decisions are made within an ethical framework such that they do indeed represent an improvement when compared to human decisions.

Algorithmic data-based decisions have the potential to improve the efficiency of governments and services by optimizing bureaucratic processes, providing real-time feedback and predicting outcomes. History has shown that human decisions are far from perfect due to conflicts of interest, corruption, greediness and bias, resulting in unfair and/or inefficient processes and outcomes. Current interest in the use of data-based algorithms can be seen as a consequence of an increased demand for greater objectivity in decision-making.

Nevertheless, data-driven decision-making algorithms are not exempt from limitations. To ensure that this new form of decision-making has a positive impact, we need to ensure that 5 key dimensions are preserved, which I refer to with the acronym FATEN: the F stands for fairness and non-discrimination; the A is for accountability, autonomy and human augmentation rather than substitution; the T is for transparency; the E is for education and beneficence, that is maximizing the positive impact with sustainability, veracity and diversity; and the N is for non-maleficence, that is, minimizing the negative impact through prudence, reliability, security and always preserving privacy.

Only when we meet these 5 dimensions we will we be able to move to a model of democratic governance based on data by and for the people. The potential to have a positive impact is immense. Hence, I believe that we should not waste this opportunity. This is what motivates my work and I hope that might inspire you to join our efforts.

— Nuria Oliver
http://www.nuriaoliver.com/