08
Nov 16

Digital Humanitarians

Shahin Madjidian

The previous posts have been somewhat negative of what big data can accomplish and the effects it may have on privacy and democracy. But are there no positive sides to it? It turns out there is!

Patrick Meier is the author of the book Digital Humanitarians: How Big Data Is Changing the Face of Humanitarian Response. In the book, he lays out a large number of examples of how he and his colleagues and friends have developed and led digital humanitarian relief and aid efforts during catastrophes with the help of big data.

It all started with the earthquake that hit Haiti in 2010. Meier’s interest in this event was sparked by the fact that his partner currently worked in the country, so he had a very personal reason for starting the digital relief effort. I think this may have been one of the main reasons why Meier continued to work with and develop digital humanitarian efforts later on – his emotional attachment to what could have been, had his partner not made it out of there.

What started as a small-time operation in a campus dorm quickly grew to something that even attracted national and international agencies. Meier himself was surprised, not only by what they were able to accomplish, but also the fact that what they did actually was possible for a beginners group like them, without any previous knowledge or expertise in the humanitarian field.

As the book progresses, Meier’s work and tools develop as he and his colleagues face scenarios with unique challenges. It is about automation in order to handle to huge amount of data that flowed in, it is about streamlining the organization, it is about educating the digital volunteers, and much more.

But the one thing that keeps returning, regardless if it is about Meier’s own efforts or the efforts of others, which the book also presents, is the importance of involving people with local knowledge. “Locals” can identify buildings, landmarks, streets and therefore become a great asset during the analysis part of the digital humanitarian effort. The “locals” also speak the language where a catastrophe has taken place, which for the most part is not English. Without this possibility to translate aid requests, Meier and his team would never be able to figure out which need was needed where.

By constantly returning to this aspect, Meier confirms the notion that big data requires local knowledge in order to properly be analyzed and used.

Another thing that constantly returned throughout the book was how smooth it was for Meier to solve the challenges that kept popping up. He always knew the right person at the right universities or agencies, which he had met during this or that conference, who knew exactly how to deal with an unexpected issue that was unique to a certain humanitarian crisis. After a few chapters it almost became absurd. It is absolutely amazing that Meier has this kind of network, but in all honesty, how many have so many great contacts? Obviously, this brings us back to the question of who big data is useful for, and who is able to handle big data.

Lastly, Meier makes sure to note that all his projects and programs are freely available. This also sounds good, but is he the future norm of big data, or rather the exception? Going back to my first blog post about ownership, I dare say Meier seems to be the grand exception, at least if status quo is kept. However, he does offer a refreshing alternative which I would hope more actors within the big data field will emulate sooner rather than later.

All in all, this book gave me a different perspective to big data and how it can be used to do good things and not only for commercial purposes. I did feel somewhat more positive writing this post compared to the previous ones. But exactly because the perspective was so different, my negative opinion of big data presented in the first two blog posts wasn’t changed much by the book. Meier doesn’t discuss privacy issue much and when he does, he believes that the ends justify the means. A critical view of ownership of big data is non-existent and there is neither a deep discussion on social media users related to power and income questions.

REFERENCES

Meier, P. 2015: Digital Humanitarians: How BIG DATA Is Changing the Face of Humanitarian Response. Boca Raton, FL: CRC Press.


01
Nov 16

Big data in an African context

Shahin Madjidian

Introduction

Many development goals, policies and programs are based on numbers and statistics. How accurate are these numbers on the African continent and can big data help in improving the accuracy?

African statistics today

In his book, Jerven offers a devastating critique over the state of statistics on the African continent. He notes that the numbers being produced and published are neither reliable nor valid, often being based on estimates, guesswork and/or assumptions. Many times, these assumptions are in turn based on older data sometimes dating back decades. These old baseline numbers have very little relevance with how things look today.

Several consequences can be identified here. Firstly, different actors may look at the same old data, but through different angles, and thus produce very different numbers (in Jerven’s case it is mostly about GDP/capita). Secondly, these poor numbers feed into a larger picture of how African countries are depicted, which problems they have and where these can be found, and any possible solutions to remedy them, to develop the nations.

Jerven laments the poor state of the countries’ statistical offices and argues that they are basically there to serve actors from the international aid, donor and development communities (Jerven 2013:105). “International institutions are the main providers and disseminators”, as he notes (Jerven 2013:8f).

Jerven calls for new baseline estimates, from which fresh statistics can be extrapolated and drawn from. However, he stresses that “these must be based on local applicability, not solely on theoretical or political preference” (Jerven 2013:xiii) and also highlights the importance and necessity of local knowledge and input. Data and statistics ought to serve the needs of the people on the ground, not reaching targets for some faraway aid organization.

Big data replacing statistics?

Can big data replace the poor state of statistics on the African continent and help improve public policy and development goals? First, let us quickly go through what big data is and how it works, before answering the question.

In their book, Mayer-Schönberger & Cukier provide us with a clear overview of big data and what it is. They note that “at its core, big data is about predictions” (Mayer-Schönberger & Cukier 2013:11), about inferring probabilities. Furthermore, big data is about finding the general direction, about a trade-off between being accurate at the micro level versus gaining insights at the macro level (Mayer-Schönberger & Cukier 2013:12f). So far, big data seems like a useful tool to use. In fact, big data can be viewed as pure statistics. But which data can be and is currently collected in big data sets?

A lot of the data comes from using various communication tools, such as cell phones and computers, while simultaneously being connected to the Internet. Taylor & Schroeder warn us when they point out that far from everyone use cell phones or is connected to the Internet in developing countries. This results in user bias and a situation where vulnerable or ‘hidden’ populations, such as children, the elderly and the poorest in society are left out in the data collection (Taylor & Schroeder 2015:510f). They argue that “mobile phone use is highly differentiated by gender and income level” in India (Taylor & Schroeder 2016:506), and a qualified guess is that many African countries exhibit the same patterns.

Meier concurs, saying that “not everyone is on social media. In fact, social media users tend to represent a very distinct demographic, one that is younger, more urban, and more affluent than the norm” (Meier 2015:37). So perhaps inferring national probabilities from a rather narrow subset of the population is a fairly poor idea, which will not give a rewarding big picture, as is one of big data’s positive sides.

Quality of analysis

If the previous section discussed the quality of data, this will delve deeper into the quality of analysis regarding big data. In the previous post I briefly mentioned how big data actors mostly are big corporations and governments. What they have in common is that the majority of them are based in the global North, far away from the realities of Africa.

Jerven writes: “In order to employ the evidence usefully, one must know the conditions under which the data were produced. This is readily recognized in qualitative analysis, but somehow these principles have not been applied to quantitative evidence” (Jerven 2013:7).

Read, Taithe & MacGinty are even more pessimistic and question the quality, reliability and validity of data when “field level information may be sent to headquarters in a different country, collated with other data and then sent back to the country of operation” (Read, Taithe & MacGinty 2016:7). They continue saying that there is a risk where people analyzing the data are cut-off from local knowledge and context, only looking at numbers (Read, Taithe & MacGinty 2016:12).

Mayer-Schönberger & Cukier in turn touch upon the very real possibility of a situation where “data-driven decisions are poised to augment or overrule human judgment” (Mayer-Schönberger & Cukier 2013:141). Let us hand over everything to the machines!

Big data the statistical saviour?

Based on the literature reviewed here, this question can only be answered with a resounding no. Jerven complained about the dominance of outsiders when producing statistics and I cannot see how things would be any different if big data actors were to run the show instead of today’s powerhouses within the statistical field. The same objections, such as democratic deficit and out-of-touch with local circumstances, can be raised and more, such as the gender and income gap among users, may even be added.

Big data proponents argue that big data “offers new and higher knowledge ‘with the aura of truth, objectivity, and accuracy’” (Read, Taithe & MacGinty 2016:10). But statistics, be it presented as big data or traditional surveys carried out on the ground, is always subjected to human bias. This is actually something that Meier, himself a big proponent of big data, confirms when he says that everything is biased (Meier 2015:39).

REFERENCES

Jerven, M. 2013: Poor Numbers: How We Are Misled By African Development Statistics and What To Do About it. Ithaca, NY: Cornell University Press.

Mayer-Schönberger, V., Cukier, K. 2013: Big Data: A Revolution That Will Transform How We Live, Work, and Think. London: John Murray Publishers.

Meier, P. 2015: Digital Humanitarians: How BIG DATA Is Changing the Face of Humanitarian Response. Boca Raton, FL: CRC Press.

Read, R., Taithe, B., MacGinty, R. 2016: Data hubris? Humanitarian information systems and the mirage of technology, Third World Quarterly, forthcoming.

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


01
Nov 16

Big data, privacy and ownership

Shahin Madjidian

Introduction

Studying big data critically leads to several interesting topics which can be examined and developed. In a string of four blog posts this is exactly what I will do. The first post is perhaps the heaviest as it deals with ownership issues, democracy and whether or not big data can be seen as something revolutionary that will lead to social change.

I will start with a short analysis of the individual’s right to his/her data and then move on to the macro level – who owns the data, who can store it, analyze it or draw conclusions from it, and later act on them?

Privacy

Today, most people in the world leave digital fingerprints as we go on about our businesses, whether we like it or not. This data gets stored and many times analyzed and acted upon by the actors who picked up the data in the first place. The data can be anonymized and used in a big data set where it is very difficult, or even outright impossible, to identify individuals, or it can be used to improve targeted commercial ads suited for a unique individual.

Privacy issues have been raised, especially as individuals have very few possibilities to reject the data collection. Spratt & Baker argue that algorithm transparency is important, as well as the fact that people should be allowed to know which data is stored on them and where. They suggest that “all individuals have the right to control their own personal data, and can choose to sell as much or as little of this as they like” (Spratt & Baker 2016:30). While this sounds laudable, there are several problems which are ignored. How will an individual get access to this data and where can s/he store it? What about situations when the individual requires money or are in other desperate situations and decide to sell data, doing a trade-off between short-term gain in favour of perhaps long-term exposure? Which population sectors in which countries may be most prone to do this?

In reality, as Spratt & Baker note, consumers may object to their personal data being bought and sold, but in reality have very little control over it once it has been collected (Spratt & Baker 2016:12). This leads us to the next section, namely who these holders of data are.

Ownership and “usership”

The main holders and owners of data today are big corporations, especially those in the social media and communication sectors, and government. Often, data collected from actions and events are used to create new forms of value in innovative ways, as “the system takes information generated for one purpose and re-uses it for another” (Mayer & Schönberger 2013:97, 103). The trick is in finding secondary usage of the data and as a result, hidden correlations which may turn out to be highly valuable. Related to the privacy issue, it is very difficult to prohibit something that has not yet happened, to prohibit uses of data which the data owners have not previously thought about.

Read et al. argue that “if the power of initiative, design, funding and analysis still resides with the tech-savvy individuals and organisations based in the global North, then it is difficult to concur with the view that technology is empowering or liberating” (Read, Taithe & MacGinty 2016:12).  This notion is amplified in the global South considering “the growth of private-sector involvement in public infrastructure projects across the globe” (Lovink & Zehle 2005:10), with infrastructure here broadly meaning Internet and cellphone development.

A few huge corporations have taken the lead in the use of big data and to remedy this, Spratt & Baker propose state support to startup companies within the field in order to learn and become more competitive (Spratt & Baker 2016:26).

Are there any possibilities of individuals becoming owners, analyzers and users of big data? Meier certainly believes so, and I will return to his book “Digital humanitarians” in a later post. For now, I will use his own words against him, as he writes that big data can easily turn into information overload and that the data coming in during one of his humanitarian efforts was simply too much for him and his hundreds of volunteer to handle (Meier 2015:4, 50, 52).

It is not only the vast amount of data that makes it difficult or impossible for individuals to use, but also its messiness and complexity. The data comes from different sources, in a wide variety of shape and form, many times unclear and fragmented. The technologies required means that big data use today is limited to a few actors. Individuals, or groups of individuals, are usually not among the lucky ones.

Mayer & Schönberger have a somewhat romantic view of the future development, believing that just like everyone with cell phones has the potential of being a “journalist” in the broad sense, everyone may be able to extract and analyze big data as “tools get better and easier to use” (Mayer & Schönberger 2013:134). It may not be necessary to be a statistician, engineer or software developer working for a government agency or Facebook.

Conclusion

While development may allow more people to become big data users, today’s actors will have a huge head start. Furthermore, Mayer & Schönberger predict that data owners will increasingly be in the most lucrative position in the future (Mayer & Schönberger 2013:134) and as long as the privacy laws are not changed, the data owners will be social media and communication corporations, not individual citizens.

I agree with the somewhat glum view of “although cloaked in an the language of empowerment, data technology may be based on an ersatz participative logic in which local communities feed data into the machine /…/ but have little leverage on the design or deployment of the technology” (Read, Taithe & MacGinty 2016:11).

In many ways, big data is revolutionary and holds great possibilities for humankind, but used within today’s societal and economic logic, it is but a furthering and strengthening of the status quo, with little to none possibility of empowering individuals or inciting social change.

REFERENCES

Lovink, G. & Zehle, S. (eds.) 2005: The Incommunicado Reader. Amsterdam: Institute of Network Cultures

Mayer-Schönberger, V., Cukier, K. 2013: Big Data: A Revolution That Will Transform How We Live, Work, and Think. London: John Murray Publishers.

Meier, P. 2015: Digital Humanitarians: How BIG DATA Is Changing the Face of Humanitarian Response. Boca Raton, FL: CRC Press.

Read, R., Taithe, B., MacGinty, R. 2016: Data hubris? Humanitarian information systems and the mirage of technology, Third World Quarterly, forthcoming

Spratt, S, & Baker, J. 2016: Big Data and International Development: Impacts, Scenarios and Policy Options. Brighton: IDS.


07
Sep 16

Hello world!

A whole new experience starts today for our group