Bias in big data: How to find it and mitigate influence

Bias can occur along different parts of the data pipeline. There is a lot of focus on potential bias that occurs during analytics, but bias in big data can be inserted or interpreted even earlier in the data pipeline.

Bias has the potential to enter the data lifecycle as early as collection, according to Kelly Capatosto, senior research associate at the Kirwan Institute for the Study of Race and Ethnicity at The Ohio State University.

“If someone is generating the use of surveys that are subsequently going to inform how a program, model or algorithm operates, [designers’] preconceived notions could end up baked into the process,” she said.

There has been growing scrutiny of large data sets and the amount of bias they contain. And while there can be advantages to intentional bias in areas such as target marketing, where a bias in data can provide more direct insight, bias in big data can quickly become an issue for business.

Here are some ways to find and mitigate bias that puts enterprises at a disadvantage.

Where to find it “[Bias enters data] early in the lifecycle,” said Mike Leone, senior analyst at Enterprise Strategy Group (ESG). This bias can be included unintentionally, even during data collection. As Capatosto said, certain biases may be ingrained into the surveys used to collect the data, but bias can also enter the collection process due to access barriers. One example of this is the recent U.S. Census. There are barriers around language, around poverty — just access to technology — that make it difficult to meaningfully incorporate that kind of information into any given process. Kelly CapatostoSenior research associate, Kirwan Institute for the Study of Race and Ethnicity at The Ohio State University “There are barriers around language, around poverty — just access to technology — that make it difficult to meaningfully incorporate that kind of information into any given process,” Capatosto said. Another thing to look for is conflation. Capatosto said one of the most prominent issues with algorithmic bias is conflating identity with a level of risk. “This is really important in the healthcare context [with] contact tracing and how to utilize big data and other measures to build out the public landscape and infrastructure around health concerns,” Capatosto said. She referenced a recent study that found certain communities were alleged to have more prevalent healthcare needs, but the parameter for those needs was money spent on healthcare.