Math is racist: How data is driving inequality
Rawlins, A. (2016). Math is racist: How data is driving inequality. [online] CNNMoney. Available at: http://money.cnn.com/2016/09/06/technology/weapons-of-math-destruction/index.html [Accessed 12 Apr. 2018].
Discriminatory practices in lending in the past has resulted in a huge wealth divide between white and black people.
This article reinforces my hypothesis, that the use of algorithmic decision-making limits individuals’ ability to progress up the economic later. O’Neil says that poor people are likely to have bad credit, live in neighbourhoods where more crime than average occurs and other negative factors, and the use of profiling algorithms online means they are then sent targeted advertising, eg. For subprime loans, certain schools etc. This further exploits them, resulting in a destructive cycle of exploitation meaning they are stuck endlessly in the same socioeconomic group.
She highlights that big data processes work on past behaviour, and so are likely to ensure that the structure of society does not change. Therefore it is essential that humans work together with data to supplement policies and decisions rather than relying solely on algorithms to do the job.
How Big Data Drives Economic Inequality
Datajustice.org. (2015). How Big Data Drives Economic Inequality | Data Justice. [online] Available at: http://www.datajustice.org/site/how-big-data-drives-economic-inequality-0 [Accessed 12 Apr. 2018].
This article expresses concern about so much information about individuals being transferrend into a few corporate hands, leading to huge information asymmetry which manifests as economic inequality. The writer asserts that big data platforms have an incentive to ‘violate user privacy’ by selling their data and this also ensures that they have less competition as the revenue generated is used to further enhance the platform. Of course, this is helped by the fact that there is little regulation in place. In fact, there is little awareness or perhaps concern about this lack of data security. An example of this is the lead-up to the Financial Crisis in 2008, when there was an increase in advertising for subprime mortgages.
The writer goes on to suggest that part of the solution could be by the government introducing regulatory tools, to ensure individuals have some amount of control over their data. Though a good idea, I’m unsure about how effective this would be as many users know about their data being shared with third-parties but are not concerned about it and so continue using the platforms regardless (as with Facebook).
I enjoyed reading more about discrimination that can occur with relation to information (‘information-based inequality’) as it has strong ties with economics, demonstrating an example of market failure. This inequality fuels other forms of inequality – such as discrimination in employment or opportunity, or advertising therefore the information gap must be corrected by authorities to ensure a level playing field between consumers and firms.
LOOKING AT INEQUALITY-RELATED MEASURES
Ons.gov.uk. (2018). Household disposable income and inequality in the UK – Office for National Statistics. [online] Available at: https://www.ons.gov.uk/peoplepopulationandcommunity/personalandhouseholdfinances/incomeandwealth/bulletins/householddisposableincomeandinequality/financialyearending2017#gradual-decline-in-income-inequality-over-the-last-decade [Accessed 12 Apr. 2018].
I looked at some of the figures from the Office for National Statistics to look at whether income inequality has been growing or falling. The Gini coefficient is a widely used measure of inequality – the closer to 0, the closer together incomes are. It is apparent from the chart above that inequality rose significantly in the 1980s, but there has been a decrease (though slow) in the last 10 years. Therefore, we can conclude that although there are still wide levels of inequality, this is about the same as there was in the mid 1980s.
Median household income and GDP per head has also shown an overall increase since 1977. However this is obviously not representative of the entire population, and eliminates the highest and lowest earners, therefore is probably not the best measure for my hypothesis which focuses on low-income groups
It might be better to look at how relative poverty has changed within the UK, or how the proportion of low-income earners has changed.
Individuals are considered to be living in relative poverty if they live in a household with a disposable income that falls below 60% of the national median in the current year.
This graph shows for how many years individuals that were at risk of poverty, showing that the large majority of the individuals escape poverty in less than a year.
Full Fact. (2018). Poverty in the UK: a guide to the facts and figures. [online] Available at: https://fullfact.org/economy/poverty-uk-guide-facts-and-figures/ [Accessed 12 Apr. 2018].
The proportion of people living in relative poverty has not changed significantly over the past 3 decades.