{"id":7562,"date":"2026-03-30T12:18:53","date_gmt":"2026-03-30T12:18:53","guid":{"rendered":"https:\/\/aomtodayprod.wpenginepowered.com\/?p=7562"},"modified":"2026-03-30T16:19:11","modified_gmt":"2026-03-30T16:19:11","slug":"data-isnt-always-neutral-confronting-bias-in-people-analytics","status":"publish","type":"post","link":"https:\/\/www.aom.org\/today\/data-isnt-always-neutral-confronting-bias-in-people-analytics\/","title":{"rendered":"Data Isn\u2019t Always Neutral: Confronting Bias in People Analytics"},"content":{"rendered":"

By Jason Collins<\/a><\/p>\n

When dealing with people analytics, relying on the notion that numbers don\u2019t lie can be misleading. The truth is that data and algorithms are not always neutral and unbiased.<\/p>\n

Academy of Management Scholar Sekou Bermiss<\/a> of the University of North Carolina\u2019s Kenan- Flagler Business School said that the biggest hurdle that organizations face is \u201crecognizing that you might have bias in your data.\u201d<\/p>\n

\u201cThis is a big leap, because if you admit to that, then you are admitting to decisions that you\u2019ve made in the past based on this data could have been faulty,\u201d Bermiss said. \u201cTo improve, organizations need to recognize that the data that we had available to us about the market was not nearly as good and maybe was biased, and now it\u2019s gotten better.\u201d<\/p>\n

Bermiss cited a study examining the promotion rates of men versus women as an example of unintentional biases in an organization\u2019s data.<\/p>\n

\u201cThe research found that men and women network differently by nature,\u201d Bermiss said. \u201cThose differences can negatively impact some people during promotion time.\u201d<\/p>\n

While useful, people analytics can often reflect the biases inherent in the system from which they originate. To confront biases in data, Bermiss suggested \u201cquestioning the virtues of your data.\u201d<\/p>\n

According to Bermiss, confronting data biases is the biggest hurdle that organizations face, and it can feel daunting.<\/p>\n

\u201cIf most of your data is probably pretty good, but there are some parts of the data that are probably prone to some sorts of biases, just focus on that,\u201d Bermiss said. \u201cWe don’t throw the baby out with the bath water.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"

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The truth is that data and algorithms are not always neutral and unbiased. 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