Most companies use machine learning algorithms to identify business opportunities and optimize marketing spend to personalizing the customer experience. These are built on the basis of historical data and well-trained prior to deployment. However, these algorithms are often marred by unintended biases.

What are these biases?

The Square-Peg Bias occurs when the algorithm is built on the wrong data. This happens when the foundation data is not the representative of the current use case and therefore your algorithm produces biased results. Go back to your foundational data to see what adjustments can be made to remove the bias.

The Wolf-in-Sheep's-Clothing Bias occurs when the metrics your algorithm uses don't mean what you think they do. This results in the systematically biased output. Consider conducting qualitative research, survey research, or additional analysis to avoid this bias.

The Has-Been Bias occurs when your algorithm starts making dated assumptions about how the world works. It is important to feed the algorithm that things change like shopping habits, computer processing speeds, the acceptability of fraud and customers' social norms. Even the slightest changes can result in significant bias.

At times, even perfectly unbiased algorithms can also become biased if they are outdated. Hence, keep an eye out for the biases to ensure that the algorithms are working fine and the way they are supposed to be.