With the resurgence of the Black Lives Matter movement on social media, many marketers are taking new notice of issues that BIPOC (an acronym that stands for Black, Indigenous, and People of Color) and allies have been talking about for years.

The most notable of these is how data – which we traditionally deem as unbiased and “just the numbers” – is in fact very influenced by the biases of the engineers, marketers, developers, and data scientists who are programming, inputting, and manipulating that data.

While some may just now notice this phenomenon, it’s actually nothing new.

In this piece, we’ll talk about:

  • What implicit bias is.
  • How it affects marketing data and technology.
  • Why it’s important to recognize it in our SEO and marketing.
  • What marketers can do.

What Is Implicit Bias?

To truly understand how these models encode bias it’s important to understand how implicit bias works.

According to research from The Ohio State University:

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“[I]mplicit bias refers to the attitudes or stereotypes that affect our understanding, actions, and decisions in an unconscious manner. These biases, which encompass both favorable and unfavorable assessments, are activated involuntarily and without an individual’s awareness or intentional control.”

To paraphrase, implicit bias is essentially the thoughts about people you didn’t know you had.

These biases can come from how we were raised, values that our families hold, individual experiences, and more.

And they’re not always **bad **stereotypes, but still, we know that individuals are individuals and stereotypes are broad and sweeping generalizations – which don’t belong in machine learning and supposedly unbiased statistics.

Think you’re immune to implicit bias?

Check out one of Harvard’s Implicit Association Tests (IATs) to see where your own implicit biases lie.

Remember that implicit bias doesn’t make you a bad person. It’s something the majority of people have.

It’s how we respond to it that matters!

#careers & education #digital experience #data analytic

Marketing & Technology Data Is Racist & Biased: Here's How Marketers Can Fix It
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