I would like to research into how the increasing use of big data analytics by firms and governments across the world correlates with poverty. The improvements that enhanced technology has made to our lives is undeniably obvious and is evident in our day-to-day lives, but its negative impacts are often hidden and we don’t realise them. Big data analytics are well-known for speeding up processes and enabling higher efficiency for firms and institutions, whether that is in recruiting new employees or predicting crime within communities.
What is not as clear, however, is the repercussions that big data analytics can have, particularly on the less privileged. This is often overlooked by citizens of first world countries who live within affluent communities. I am curious to find out whether the widespread use of analytics can in fact heighten poverty, because of the algorithms they are based upon or the way in which they reinforce the divide between the upper and lower classes. Take programs targeted to improve policing, for example. Although these algorithms are not inherently racist, they may target poorer communities, as this is statistically where issues such as vandalism, theft or drug problems occur. However, communities are made up of people from the same ethnic background, who in turn may be targeted for crimes that occur in other communities but haven’t been picked up by the algorithm as there is less crime there.
I would like to analyse different examples of where big data analytics is used and evaluate the extent of the harmfulness that they pose to society, with a particular focus on the poor, in terms of whether its benefits outweigh the drawbacks.
I came up with the following title, which will allow me to explore both sides to big data analytics in terms of socioeconomic status.
“To what extent do big data analytics exacerbate poverty?”