The Social Solutionism of Big Data

The Social Solutionism of Big Data
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I recently came across an article about an experiment where the author tries to opt out of big data. Technological solutionism and big data can be an important factor in one’s every day activities. In fact, big data is already an integral part of our lives. Our always connected devices generate data every second logging our activity and unique personal preferences that we make online.

Furthermore, our online actions as consumers produce data which in its turn can be used in the process of predicting tendencies in human behavior. In the age of data and analytics, everything we do generates data. Always on technological devices, living creatures, everything can be explained through the means of data. And it looks like all of them can store and produce data as well . Perhaps one day we will be able to create, store and consume data by ourselves and for ourselves. It seems like data is one of the top words that will characterize our century. Or at least a good part of it.

The Inevitable Solutionism

In his “To Save Everything, Click Here”, Evgeny Morozov argues that the folly of the technological solutionism leads to a world where the power of algorithms eradicates imperfection. And where the rules imposed by the Silicon Valley shape our future (Morozov, 2013).

The author provides some examples for such a technological solutionism inspired by “Zuckerberg’s tyranny of the social”. There we find evidence that “activities get better when performed socially” (Morozov, 2013). The BinCam project which makes our bins “smarter” (by taking photos of what you just have thrown away), “more social” (by uploading these photos to your Facebook account) is one of these examples that promise to save our planet.

Another interesting example that Morozov gives is the prototype teapot. It  “either glow[s] green (making tea is okay) or red (perhaps you should wait)” (Morozov, 2013) the hardware of which “queries Britain’s national grid for aggregate power-usage statistics” (Morozov, 2013).

Algorithmization of Ethics?

But as Morozov suggests, nowhere in the “academic paper that accompanies the BinCam presentation do the researchers raise any doubts about the ethics of their undoubtedly well-meaning project” (Morozov, 2013). The situation is similar to the case of the teapot prototype where “social engineers have never had so many options at their disposal” (Morozov, 2013). He further argues that resolving complex social problems with the help of the right algorithm is more likely to cause unforeseen effects and repercussions that can generate “more damage than the problems they seek to address” (Morozov, 2013).

The more big data and analytics become integral part of our lives, the more difficult it is to refuse to let technology control simple daily activities. And doing your everyday tasks the old-fashioned way seems more complex and more impossible. Even a simple attempt to opt out from marketing detection (like using Tor for browsing Facebook or Twitter) can make your online activity look suspicious and illicit (Vertesi, 2014).

But as Morozov suggests, big data without any connections to social networks can do quite positive things too. He mentions the BigBelly Solar and its positive impact on cutting “garbage-collecting sorties from 17 to 2.5 times a week” in the city of Philadelphia and the Street Bump project where, thanks to motion detectors in smartphones, an app helps with reporting potholes on the streets of the city of Boston. In other words, people use data for good or bad purposes. And the path we choose depends on our shared vision of the future of our society.

References

Morozov, E. 2013: To Save Everything, Click Here: The Folly of Technological Solutionism, New York, NY: Public Affairs.

Vertesi, J. 2014, My Experiment Opting Out of Big Data Made Me Look Like a Criminal, Last Checked: 17/10/2017, Retrieved from: http://time.com/83200/privacy-internet-big-data-opt-out/

Data for Development – contributions from the Nordic countries

OECD is one of the international institutions that makes use of data produced by our use of technology, such as mobile phones and internet, in their research and policymaking (Taylor and Schroeder, 2014, p. 505).

At the end of this month, on October 30th, OECD will release its Development Co-operation Report for 2017, Data for Development. In the introduction of the report, OECD stresses the importance of big data and the data revolution for development in a broad sense: “more and better data can help boost inclusive growth, fight inequalities and combat climate change. These data are also essential to measure and monitor progress against the Sustainable Development Goals.”

Furthermore, the report stresses the fact that the lack of good data is still evident in most developing countries, and asks central questions: Why are over half of deaths and one-third of births worldwide unaccounted for? Why is investment in statistical capacity (…) not a priority for most providers of development assistance?

The report calls for a strengthening of national statistical systems and will present measures to “make data work for development”.

The profiles of different countries’ contributions to data for development is already published. This post briefly looks at the contributions from the Nordic countries in the report: Denmark, Norway, Sweden and Finland.

Denmark
According to the report, more and better data is getting more important than earlier for Denmark when it comes to reporting on the Sustainable Development Goals (SDGs). Until now, it has not been a high priority. Denmark aims to “improve statistical production and promote the use of data by policy makers, civil society and citizens”.

During 2013-2015, Denmark committed on average 12,7 million USD per year to finance statistical capacities and systems in developing countries.

Norway
Norway contributes to strenghtening data for development through its national statistical institute, Statistics Norway. Among other things, Statistics Norway collaborates with the UN’s High Commissioner for Refugees “to establish international guidelines for collecting and producing statistics on refugees and internally displaced people.”

Norway aims to “improve statistical production and data literacy and to strengthen co-ordination among development partners” and it contributes in helping developing countries “to build up civil registration and vital statistics”.

This year, Statistics Norway participated in an expert panel meeting where it provided input to the Development Co-operation report, stressing the importance of building holistic and sustainable national statistical systems (p. 10 in Statistic Norway’s newsletter “International Development Cooperation at Statistics Norway”, June 2017).

Norway launched a white paper this year in where it stresses the importance of good data and statistics in development cooperation.

Norway committed on average 15,0 million USD per year in 2013-15 to finance statistical capacity building.

Countries where Statistics Norway has Institutional Cooperation:

Source: Statistic Norway’s newsletter, June 2017

Sweden
Data for development or building up statistical capacities is an important part of Sweden’s development work. It is included in Sweden’s Budget Bill and the new policy framework for Sweden’s development co-operation. Sweden contributes in improving statistical production and literacy in development countries and offers technical assistance and financial aid. Among other things, it gives financial support to the UN Global Pulse, “which harnesses big data for development and humanitarian action”.

Interestingly, Statistics Sweden has collaborated with Burkina Faso and Mali on establishing “continuous household surveys” to help building up statistical capacity.

Sweden committed on average 20,89 million USD per year to finance statistical capacity in 2013-15.

Finland
The OECD report does not contain any information on Finland’s contribution to data for development. Finland’s contribution this field therefore remains unanswered.

However, Finland contributed a much smaller amount of money than its Nordic neighbours (1,51 million USD per year in 2013-15) to statistical capacity building, which indicates that it is not a priority.

The full country profiles are available here: http://dx.doi.org/10.1787/dcr-2017-en

References:

OECD (2017) Development Co-operation Report 2017: Data for Development, OECD Publishing, Paris. Retrieved 2nd October 2017 from http://dx.doi.org/10.1787/dcr-2017-en

Statistisk sentralbyrå (2017): International Development Cooperation at Statistics Norway: A newsletter from Statistics Norway’s Division for Development Cooperation, June 2017. Retrieved 2nd October 2017 from
https://www.ssb.no/omssb/samarbeid/internasjonalt-utviklingssamarbeid/_attachment/316124?_ts=15d36b4d518

Taylor, L., Schroeder R. (2015): Is bigger better? The emergence of big data as a tool for international development policy. GeoJournal 80: 503-528.

Data for Data’s Sake?

”Data has become increasingly important to the way we think and talk about conflict and our humanitarian responses to it”, Read, Taithe and Ginty write in their article ”Data hubris? Humanitarian information systems and the mirage of technology” from 2016. They exemplify this by referring to the UN High Level Panel on the Post-2015 Development Agenda’s ”call for a ”data revolution””, which ”would draw on existing and new sources of data to fully integrate statistics into decision making, promote open access to, and use of, data” (UN High Level Panel; Read, Taithe and Ginty (2016, p. 1).

However, ”data is not knowledge”, as the authors of this article emphasize, and they refer to geographer Trevor Barnes’ question: are we generating useful knowledge or are we collecting ”data for data’s sake”? (Read, Taithe, Ginty, 2016, p. 2).

I can relate this very well to my experience from advising newly arrived refugees at my home town in Northern Norway. From the moment when new asylum seekers or refugees arrive in Norway, the different authorities that are involved in the processing of the refugees’ cases will begin to gather data about them and their families. Throughout their asylum process and after they are granted a residence permit, the same data will be gathered again and again because so many different bodies or stakeholders are involved. In our work, therefore, I sometimes question myself if we are collecting ”data for data’s sake”. New technology gives us opportunities to collect, store and manage data in new ways. At the same time, the requirement on data collection, together with applying new (and sometimes hard to implement) technology where the data is stored, has the tendency to be caught up in bureocratic procedures that may make our work much less efficient than it could have been.

In their article, Read et al. conclude that ”the declarations of emancipation via a data revolution are premature” (Read et al, 2016, p. 12). The cases for this conclusion may be different from my case of data gathering about refugees, however the conclusions can be applied here as well. The authors of the article suggest, among other things, an improvement of the data-processing capabilities of humanitarian organisations as well as a request to ”collect enough, but not excessive, information” (p 13). This needs to be taken into consideration in the “data revolution” that is called for by the UN High Level Panel.

This is not intended to be a pessimistic statement against the ICT for development (ICT4D) or the ”datafication” of humanitarian work, but is meant to highlight one of the challenges one is facing when the digital world meets humanitarian work or development practice. New perspectives on this will come in the following blog posts.

References: Roisin Read, Bertrand Taithe & Roger Mac Ginty (2016): Data hubris? Humanitarian information systems and the mirage of technology, Third World Quarterly.

UN High Level Panel, Economies through Sustainable Development.