The social justice movement is perhaps one of the longest-running, biggest, global humanitarian efforts we have ever faced. Most, if not all, countries, cultures, genders and races across the world are faced with an increasing intolerance to anything ‘not the norm’, but also an increasing movement to educate, ‘normalise’ and change.
Social justice is defined as the fight to break down any barriers that prevent equal opportunities being afforded to everyone, regardless of race, economic status, gender and sexual identity. It also encompasses the fight against the toxic contamination of the air and water. This includes attempting to change governmental and environmental policies in order to create more access to aspects such as healthcare, public education, social services, transportation and housing.
Using the massive datasets that are so easily accessible nowadays, along with artificial intelligence using machine learning, governments across the world have some very powerful tools at their fingertips to address socio-economic problems such as poverty, environmental safety, food production, security and the spread of disease.
It should therefore be easier than ever to analyse and find new ways to address such social problems, as well as to develop new, more efficient and effective responses to climate disasters. Sadly, however, that is not the case.
The promise made that the analysis of our data will result in a new age of fairness, transparency, cost-effectiveness and efficiency is a fallacy. This data is largely collected from our smart devices and Internet usage and at any given time of the day – we are essentially creating a digital trace.
Collated and run through powerful scoring algorithms, the way this data and information is interpreted has the power to create mass devastation while discriminating against the most vulnerable. This can lead to the rich getting richer, the favouring of a particular race over another, or the allocation of resources away from where they are most needed.
Data, and the massive anonymous algorithms that churn through it, have become a double-edged sword.
Using data to root out the race, gender, class, religion, etc, intolerance in our society requires a fundamental shift in the way the global community is educated, in the way the global mindset defines itself, but also an understanding that any progress that is made at the expense of others is a false sense of progress.
Data, and the massive anonymous algorithms that churn through it, have become a double-edged sword. Heavily relied on by decision-makers to make effective investments, improve infrastructure and streamline spending, etc, it can also be used to widen the divide – between race, gender, religion, etc; as well as to reinforce already existing objectionable and repugnant constructs and create and cement new ones.
Our time, our appointments, our social interactions, and so on, have become quantifiable and valuable and an asset. Already used to dictate policies and spend – who owns the rights to the data that is being used to supposedly improve lives? Perhaps more importantly, how do we remove the bias that is inherent in Big Data?
Simpson's paradox, which also goes by several other names, is a phenomenon in probability and statistics, in which a trend appears in several different groups of data but disappears or reverses when these groups are combined.
So, to the naked eye, a subset of a dataset or a set of statistics taken out of context may seem perfectly fine but when these patterns are viewed cumulatively, the results reveal the complete opposite. These separate trends can lead to misdirection and mask the overall and true value of data.
This is especially true when we look at the distribution of wealth, education, medical and social sciences, etc. For example, a treatment that appears effective at the population-level may, in fact, have adverse consequences within each of the population's subgroups.
So, how do we make sure that we, as data professionals, successfully and meaningfully contribute to this fight for social justice and help to eliminate discrimination? Using the myriad of reputable educational resources on how to generate and apply datasets that have the least amount of data bias possible, we have the tools that will allow us to approach data with a mindset that is already primed to look at social awareness and discrimination in greater depth than before.
Data is power. It has been used as a weapon of discrimination for a very long time. It has been used to marginalise and trivialise far too many people for far too long. However, I have to believe that in the right unbiased and ethical hands, data can also be the solution to social inequity and injustice.
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