• Cole Feix

Is Bias Bad?



Last week we published an article on fake news, and one of the questions we got in response concerns bias. Some of you asked, isn’t bias bad? Is it really unavoidable? Shouldn’t we be looking for unbiased sources? I wanted to comment on this, because it’s a really good question, and it is crucial for understanding the media situation we find ourselves in.


Let’s talk about the nature of bias.


First of all, as a culture we don’t believe biases are bad; we believe bad biases are bad. For example, you probably remember last year when the “Frappuccino hit the fan” at a Philadelphia Starbucks, and the company responded by closing 8,000 stores for a day and sending the entire staff through unconscious bias training. Now, let’s be clear, what happened was horrible and inexcusable. No one should be profiled anywhere, and the circumstances of that incident were particularly egregious. However, Starbucks’ response is what relates to our present topic. Why did Starbucks send their staff through bias training, when by all reports, it is not effective? (See NYMag and an excellent article I lifted the frap line from at Quillette).


They did it partially because bias has a reputation for being bad. The term carries a derogatory tone culturally. But even more likely, Starbucks found the ultimate win-win for their reputation rehabilitation. First, unconscious bias training, or implicit association tests, are culturally acceptable. This is probably short-lived; shrewd observers are noticing (in the pages of the NYT) that unconscious bias training completely lets racist behavior off the hook, treating it as an implicit reaction. Second, this kind of reaction is a strong statement to every single Starbucks employee. The company means business. Rightly so. But it doesn’t actually address the real problem. What the executives did was punish their employees for bad behavior. What they did not do was solve the problem.


What this encounter highlights is that it isn’t so much that bias itself is a problem, it’s that specific biases are a problem. What unconscious bias training hope to do is identify and root out harmful bias by replacing it with other biases. So we don’t believe for a moment that the opposite of a racist bias is an objective vacuum of bias; the opposite is a non-racist bias.


Second, not all biases lie on a moral spectrum. Some biases are amoral. In these cases, accuracy is a better category to evaluate bias. In one of its most basic forms, bias comes down to ordering. Take a line of 10 people as an example. There are an infinite number of criteria we could use to order this line of people.* For example, we could go tall to short, old to young, order of birthdays, number of freckles, favorite number (this is one way we could show it’s infinite), or any other way we want. The question as we order them is whether or not we’ve accurately applied our ordering standard.


What we’re after in this example is accuracy, and this is an important principle when it comes to bias. Is what we’re reading accurately presenting the information? Can we get any hint as to what the ordering principle might have been? What kind of worldview does this author have? What assumptions are they making as they analyze the data? These are amoral questions that are extremely important. We should be able to learn something from any construal of data, if it’s accurate. Consider these two headlines, “Trump Administration Separates Families at Border,” and, “Illegal Immigrants Endanger Children by Crossing the Border.” Both of these headlines are accurate, but I would imagine we all gravitate to one over the other. This is a difference in the principle we’ve used to order the data.


Back to Fake News

With these two thoughts in mind, we should be able to navigate anything we hear or read by assessing the way bias is functioning. This is where fake news comes in. The difference between fake news and legitimate news comes down to accuracy. Fake news is either patently untrue or an inaccurate construal of the data. The difference between news that you agree with and news you don’t comes down to bias. Without the effort of sorting through biases and learning to see the way other people see the world, we’ll become more entrenched than we already are.


To reiterate a couple of principles for navigating the news, here are two things to keep in mind. First, we should recognize that everybody is coming from somewhere. We have to take every perspective for what it is. There are some perspectives that we don’t want to entertain; racism is a great example. Racism is an immoral bias, and it is wrong to construe things that way. Secondly, we should look for accuracy. Is this person doing justice to the data? Selectivity is something that is inevitable in the world of social media, but is this person intentionally distorting the data?


Bias is not all bad. When it comes to news, it’s important to look for data, not just interpretation. Do the hard work. It’s important to get to interpret the data on your own. When it comes to bias, make sure you think about where they’re coming from, especially when it comes to narratives, bias’ older more comprehensive brother. Some of the most dangerous biases are ones pretending not to exist.



*Some might object that there is a finite number of orders for this group. That’s true if you’re talking about unique outcomes. There are 10! or 3,628,800. What we’re talking about here though is the number of criteria we can use to order the group, which is infinite, even though it will produce multiple lines ordered the same way.



Cole Feix is the founder of So We Speak and a regular writer. Follow him on Twitter, @cfeix7.


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