Data Science Career is the hottest and most demanded topic in the market among the youth in 2023. Data science includes advanced analytics techniques and scientific principles to extract valuable information from data for business decision-making, strategic planning, and other uses.
Data science as a discipline has received an increasing amount of attention in the public consciousness over the past decade. Whether it be undergraduate students considering lucrative career paths or leaders of companies and organizations looking for efficient tools to inform or automate important decisions. While focusing on data science may build one’s tech career or optimize a company’s service, we have largely failed as a discipline to acknowledge what role data science plays wherever it’s practiced: When someone practices data science, they are either challenging or enforcing an existing structure of power.
This plays out on both the individual and collective levels that make up our society: On a collective level, it’s incumbent on organizations to fill their data science teams with data scientists who represent a well-rounded palette of diverse backgrounds and life experiences (and that’s no easy task right now). On an individual level, it’s every data scientist’s responsibility to understand their place in relation to human-made systems of power so that they don’t unwittingly act as an oppressor through their practice.
Today, industrial data science is practiced through processing immense amounts of data to create patterns of automated decision-making called models. These models ultimately provide automated decision-making capabilities, saving an enterprise time and money. On its face, this isn’t in itself a bad thing, It has proven to be a powerful way to build useful customized product experiences or optimize the inner workings of an organization. We have industrial data science to thank for efficient food delivery apps, search engines, and automated recommendations of all sorts, to name a few benefits.
Data scientists working on this project did not have enough awareness about how racial biases in the criminal justice system are reflected in the data they used to train their recidivism model.
This illustrates how data scientists who grasp the mechanics of their work but not the work’s context don’t have enough information to do their jobs effectively.
Within the landscape of industrial data science, a data scientist is often nothing more than an interchangeable person who knows how to work with data to develop models, rather than someone with a specific background in the subject matter of the given data. This is what leaves data science as a discipline so blind to its own effects. The result is that data scientists and their employers often fail to consider their context within systems of power, which ultimately perpetuates systemic inequities.
The result is an imbalance of power in favor of the minority who owns the data and decides how it’s used. Industrial data science is practiced with an eye for optimization rather than comprehension. All too often, data science is used as a means of automating decisions within a system rather than a means of more deeply understanding those decisions and their contexts. This is dangerous because it leads to short-sighted decision-making that fails to check its effect in the world beyond whether or not an immediate (often) profit-driven goal was achieved. It ultimately emphasizes the particular needs and worldviews of those who architect data science-driven systems.
It is important to acknowledge two important problems. Data science is not just a quantitative field when it comes to applications related to humans. With the above examples of data science applications, it is clear that data scientists responsible for designing those systems lack the ability to detect harms and biases in their systems once they’ve been released into the world.
Representation within the workplace leads to representation within the data. Both concepts are interconnected. Conversely, representation within the data can ultimately contribute to a society that yields a more egalitarian hiring pool.
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