The Data Divide
How The Excluded Become The Most Surveilled
As information technology keep advancing rapidly, data collection on people has also been affected to the point that most digital product & services revolves around data collected about us. Not only does it allow for easier data collection by replacing paper forms with various electronic channels, it also enables novel techniques to be used to link this trove of new data to generate insights to be used to improve businesses and produce better decision making. Owing to its nature, the general narrative is that a widening “data gap” exists because most of these new businesses overwhelmingly targets the educated and affluent, two widely accepted major criteria that affects computer and internet usage the most. There are valid concerns that while internet promotes greater equality, the very people who would have been benefited the most are largely excluded and left behind.
However, as the public sector begin to embrace these advances and apply it to their operation, a paradox of information asymmetry begin to emerge: that both public and public sector entities actually collects more data on the poor and marginalized. An FTC report, “Big Data: A Tool for Inclusion or Exclusion?” , published on February 2016 explored this issue noting that the segment of the population is among the most surveilled owing to various government assistance program collecting numerous sensitive data as part of their business process. While, for example, a middle class household left their digital footprint as a proxy for shopping behavior when they ordered clothes from Amazon, a low-income family has to provide additional sensitive information on their financial position to determine their eligibility for government assistance. Tax preparation software providers and banks, who often waiving service fees for those earning below certain threshold, also collects data on every step of the process and potentially aggregate them through data exchange agreement. One possible use gained by analyzing this data is to differentiate insurance premium based on metrics of risk derived from not only conventional income data but also new signals produced by these new data collection.
In the time where data collection is a de facto situation and our attention span can’t cope with the complexity of the whole system, we tend to click ‘Agree’ without understanding the consequences why. For the poor, the unknown become larger while at the same time they are getting more exposed to the dark side of big data that seemingly being used to differentiate, target, and exclude them from the race to equality.
