The Hidden Biases in Big Data


Image credit: Harvard Business Review.

SMC Principal Researcher Kate Crawford reached the number-one slot on the “Most Read” list of the Harvard Business Review this week with her sharp and insightful blog post on the weaknesses of big data.

Debunking the commonly held belief that “numbers speak for themselves” in large data sets, Kate brings the voice of reason to utopian and determinist claims that reams of “raw” data are the solution for a multitude of societal ills:

Data and data sets are not objective; they are creations of human design. We give numbers their voice, draw inferences from them, and define their meaning through our interpretations. Hidden biases in both the collection and analysis stages present considerable risks, and are as important to the big-data equation as the numbers themselves.

Kate goes on to argue that while they may seem abstract, data sets are “intricately linked to physical place and human culture”, and that both qualitative methods and computational social science will need to join forces in order to fulfill the true potential of big data science:  “data with depth”.

To read the full piece, click here.

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