An honest evaluation of where preconceptions and biases exist within an organization and therefore in its data, which influences our outreach is the first step.
And, because machine learning is designed to predict the results users want, inherent bias is further validated and even deepened.
To mitigate this, marketers must ask hard questions of themselves and about their audiences, in terms of what preconceptions exist.
While successful marketers must establish a strong understanding of AIs data capabilities, complicated problems can only be solved when hard findings are weighed against human perspectives. This blend of automation and human touch means a broad range of variables, including gender, race, sexual orientation and more, are more likely to be considered.
If marketing professionals embrace societys diversity and invite it into their programs, bias is more likely to be illuminated and challenged.
Consider a university that inadvertently screens out an ethnic group because historically not many students from that group applied to that school.
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