hiddenbias

Data and data sets are not objective, writes Kate Crawford, principal researcher at Microsoft Research, in the Harvard Business Review.

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.

She argues that the next frontier is how to address these weaknesses in big data science.

“Social science methodologies may make the challenge of understanding big data more complex, but they also bring context-awareness to our research to address serious signal problems. Then we can move from the focus on merely “big” data towards something more three-dimensional: data with depth.”