Thoughts are free, who can guess them,
they flee past like nightime shadows.
No one can know them, no hunter can kill them
It remains: The thoughts are free!
I think what I want and what makes me happy,
but always inwardly, and as it suits.
My wish, my desire no one can deny,
It remains: The thoughts are free!
The following report on the Hot Topic at the 3rd HLF in 2015 was written by the session moderator, Michele Catanzaro.
The traditional German song quoted above, Die Gedanken sind frei (Thoughts are free), has popped up again and again in liberty fights throughout the country’s history – from the 1848 German revolution to the anti-Nazi resistance. In its first lines, it neatly declares the deep connection between freedom of thought and privacy.
But as information technology becomes more and more embedded in everyday life, our thoughts, wishes, and desires are not as private anymore. They are tracked, modelled, and maybe even nudged, through data-driven technologies.
Massive amounts of data about us are collected from portable devices, Internet navigation, electronic registries, and all sort of digital tools and databases. This information is used in advertising, insurance, healthcare, increasing work productivity, fighting terrorism and crime, and so on.
The penetration of information technology in all aspects of life has spurred a long series of worrying stories: privacy intrusions by companies, anonymity reversed by hackers and “Big Brother” control by organizations like the US’ National Security Agency (NSA).
At the same time, data-driven technologies are behind some of the most exciting advances of our times: findings in astronomy and genetics, personalized medicine, disaster preparedness, energy efficiency, better company management, data journalism, and even “Twitter revolutions”, just to name a few.
How to make the most out of Big Data without incurring its most sinister aspects was the subject of the 3rd Heidelberg Laureate Forum’s Hot Topic session, on 25 August 2015 in Heidelberg (Germany). A group of panellists from the academia, industry, and civil society organizations met laureates and young researchers to discuss the ethical and societal challenges associated with Big Data. The extraordinary concentration of top-level scientists and talented youth in a free-thinking atmosphere resulted in a well-grounded discussion without red tape.
Facing these issues is not only a moral imperative for researchers involved in the field, but it is also a strategic move: computational science is surrounded by a halo of omnipotence and suspicion, which could interfere with its many beneficial effects. The polarization that has characterized the discussion around GMO and nanotechnology versus the relative balance around stem cells or IVF is partially due to the promptness with which the scientific community engaged in related discussions. The young researchers that joined the event were enthusiastic about it: hopefully, these scientists in the early phase of their careers will become proactive and constructive allies to the public in the debate around the social challenges of computational science.
The benefits of Big Data largely outweigh the challenges: the latter must be tackled precisely to make the most out of the first. Big Data for disaster preparedness is an example of it.
Director of the Data Science Initiative at Microsoft Research Outreach. Tolle is one of the editors and authors of a seminal book on data science, The Fourth Paradigm: Data Intensive Scientific Discovery.
“We all know that water brings life, but we must realize that it can be dangerous: flooding causes more deaths than any other type of natural disaster. In the United States, on average, floods cost the national flood insurance agency more than $8.4B per year,” said Kristin Tolle in her plenary talk. However, combining data generated by geologic, hydrologic and meteorological services, may ease the effects of those generated by water related disasters, thanks to the National Flood Interoperability Experiment (NFIE). Its objective is aggregation of various open data from governmental agencies to create flood forecasts, which can, in turn, reduce the damage of floods and prevent loss of life. This application shows that the benefits of Big Data and predictive analytics largely outweigh the challenges, according to Tolle: the latter must be tackled precisely to make the most out of the first.
“In the US, each agency must share taxpayer’s generated information as open data, but they are mandated to not to hold each other’s data,” said Tolle. Public information include USGS’ historical data, USACE’s reservoir data, National Weather Service’s precipitation models along with FEMA’s flood inundation maps. “It’s all open, and nothing stops you from pulling them together. The challenge is performing a spatial and temporal aggregation so that you can share the collective data products and predictions through cloud-based web services,” said Tolle.