Yesterday I took part in the department of Development Informatics big data event at the University of Manchester.
Really interesting discussion. Made me think more carefully about what effect the blind-spots in my data will have, and how collecting data can make these blind spots worse.
At the end of the day we had a bit of a discussion. Our group was particularly concerned by the effect on power shifts or concentrations with increased data aggregation (similar to Neil Lawrence’s Digital Oligarchy).
We started with the question “What will be done with the data?” and then “How does it become information?”
This led to the obvious point that it depends on who can use it, which then reinforces the power-shift that we started from. The outcome of “How does it become information” leads to the question “How can using data actually foster development? (and avoid inequality)”. We also had as concerns around the transformation from data to information that it is often bias or focused on inanimate or simple things (measuring the water pump rather than the people).
We finally looked at how to change or stop the shift or concentration in power. Two options presented themselves, either to stop using the data, and halt the path to large scale big-data analyses. This seems implausible, given the path we are on. A second option was “Can [the power shift] be mitigated by giving everyone access?”, in other words, will open data save us from the digital oligarchy?
This was again criticised; how can an illiterate farmer or boda-boda driver engage or use large data sets?
My own view is that we need layers of intermediary; from the machine learning/analysis experts who can combine and use the data, and visualise it in clear ways, to journalists and civil society who effectively ‘represent’ the citizen. Our concern is that the machine learning expert is a very particular part of society: usually white, highly educated, young and male. We can go some-way to mitigate that by investing in and supporting MSc and PhD level education in developing countries… however, I’m aware the students at Makerere (for example) were not a ‘typical’ sample of the Ugandan population. Most of the population is rural, with a good proportion unable to read or write. I suspect that the Ugandan students will represent their country-men and women little better than a muzungu. However, it is a start down the path, towards some form of democratic or universal access to the power provided by machine learning and big-data.