Inequities in Data Access
While biology may have taught you that the mitochondria is the powerhouse of the cell, what it didn’t teach you is that wealthy institutions have created a hierarchy of ‘big data’ privilege. Institutional research has widened the crevasse between the “Big Data rich” and the “Big Data poor”.
As a student research assistant at the University of Michigan, I have seen the privilege needed for big data access first-hand. To access the necessary data for my research project, I partook in 2-week long departmental training and in a grueling 3-week long process to become a US Department of Veterans Affairs employee — all so that I could have access to an extensive supply of confidential hospital information. Thankfully, the university had the funding and resources to support my department throughout this process.
It is disheartening to know that I have played a role in solidifying this hierarchy of privileged access in institutional research by simply participating, yet encouraging that my awareness serves as a starting point for change.
As Boyd and Crawford point out in Critical Questions for Big Data, there is a “new kind of digital divide: the Big Data rich and the Big Data poor”. Large, wealthy institutions oftentimes have the most leverage in being able to access the most private and costly datasets available. Being part of a research group at this financial powerhouse of a research institution comes with a lot of data access privileges as evidenced by the time and money spent on my training and certifications for employee status. These researchers are historically predominantly white and of other privileged social identities and often, knowingly or not, demonstrate implicit biases in their research. The privileged institutions perpetuate the cycle of hiring researchers of similar privilege. It is thus important to understand and outline these limitations and biases to prevent misinterpretation.
This hierarchy of privileged data access in turn produces a restricted culture of research findings. In D’Ignazio and Klein’s Data Feminism, they emphasize the importance of interpretation in analyzing data — “all knowledge is situated”. The dominant and privileged groups who more often have access to big data determine what conclusions are drawn and can inject harmful bias into their analysis. Given the hierarchical nature of this field that the authors highlighted, it only continues to build upon itself to show biases in many aspects of information technology. If we can find a way to narrow this gap and diversify the composition of the “Big Data Rich”, we can begin to eliminate the inherent biases of researchers.
