What can be done?
Brands have come a long way throughout the years. With help from new technology, they’re able to grow and reach more consumers than ever. Although, technology isn’t perfect. Recently, Google and other individuals took a dive into these machines and the algorithms they use. Their findings showed these devices try to assume who we are as buyers, and some of these assumptions are able to distort the customers interested in a brand.
The technical term for this issue is called data bias. In most cases, machine learning is used to benefit customers, but unfortunately machine learning bias can create a negative outlook on the brand and push consumers away. This all stems from the type of data put into the machine and how it interprets it. For example, if 100 people buy a product, and only 10 of the buyers are female, the machine will take this data and assume that more male customers will buy the product. Machines don’t have a brain or conscience to consider what’s right or wrong, they base their moves on the data received. This creates larger issues for a wider range of customers. Similar issues may happen with customers of different races and age.
Knowing this to be the case, how are brands supposed to combat data bias?
Simple. You need physical human teams to consider, find, and fix these problems. It’s up to these teams to implement different ways for machine learning to consider every individual interested in a brand. Brands are not trying to specifically target certain groups or create other demographic. That’s developing because of the data the machines have received throughout usage. While these issues are tackled, it’s important for the human teams to collaborate with their customers to let them know they are aware of the issues and to be transparent.
The best way to correct these issues are by including more individuals in helping correct machine learning. More minds can help create and test more solutions while more data can help correct data bias. We’re fighting discrimination more than ever, and even with our data there are reminders of these issues. Facts like these examples should never be assumed: a black customer may not be poor, a white customer may not be rich, a male customer may not want female products, a female customer may not want male products, and so on. Each person is different from one another, with their own interests, hobbies, preferences, lifestyle, and feelings.
It’s up to businesses, brands, and, frankly, everyone to correct issues like these. It’s not the goal of the brand to bring upon the situations of bias. Instead, it’s beneficial to find solutions so we can correct the ways of machine learning and true fairness.
Machine Learning and True Fairness
Interested in learning more about machine learning and true fairness, go to Think with Google and take a look at some of the research papers on the subject.
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