Decisions are then sometimes made via unintentional signals (like age, race, or gender) which introduce bias to the campaign. For example: Analyzing which users click an ad is only a fraction of the intended audience.ĬMOs often lack insight into how these algorithms are working and what biases may be built into the models. Companies are forced to look into their own datasets to determine what is being collected.
However, as the Federal Trade Commission (link resides outside of ibm.com) noted, machine learning can result in bias and discrimination against marginalized groups. Marketers using AI technology strive to remain objective in their decisions. The state of bias in advertisingĪs the industry increasingly relies on tools to automatically segment audiences and run campaigns, more and more decisions are being made by machines.
These tools then influence who the campaign targets, and can cause certain groups to either be advantaged or disadvantaged over others. Although marketers try to avoid preliminary judgments in their decision-making, it can be a difficult task to achieve.Īdditionally, advertising technology tools are created and trained by people who have their own unconscious biases. Advertising bias relies on assumptions, instead of objective truth, to carry out campaigns.
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