Today, more and more marketing tools are AI-powered. As that shift has occurred, marketers are grappling with the fact that there will always be some form of unintentional algorithmic bias affecting those platforms. The bias is programmed even without data science teams realizing it, making it difficult to detect and resolve.


Copyright: – “5 steps to minimize AI bias in marketing”


As marketers, we inherit the biases in the algorithms we use for advertising, whether they’re algorithms we build or buy. Thus, it’s important to develop concrete steps to ensure minimal bias in the algorithms we use, whether it’s your own AI or AI solution from vendors. AI, particularly machine learning, already enhances a wide range of marketing solutions including hypersegmentation, dynamic creative, inventory quality filtering, dynamic sites, and landing pages. But there are lots of things that can get in the way of an algorithm’s success.

When bias sneaks into AI, it can wreak havoc on efforts and campaigns in a variety of ways. This often happens because marketers have better or more data about some situations or customers than others, and that leads an algorithm toward being more accurate for the ones with greater data volume. Here are some common examples:

  • We all want to “conquest” competitors’ customers, but marketers usually have better information about existing customers than future prospects. As a result, there can be a fair amount of risk that those algorithms are inherently more successful at finding people just like their current customers.
  • Many marketers segment and target high-value customers. Since there is likely to be fewer of those, algorithms are typically trained mostly on data from the more common, lower-value customers. Consequently, those algorithms prove to be biased toward finding lower-value customers, hurting efforts overall.
  • Marketers may have trouble optimizing marketing for late-adopting customers when early adopters make up most of the customer base for a newer product. This can easily occur, because it’s primarily the early adopters’ data that will be used to train the algorithm.
  • Marketers might inadvertently prioritize inventory on shorter tail apps because the algorithms we use for bid optimization had more training data from those apps than from others.

A key lesson here is that we can’t take AI algorithms at face value — and they’re certainly not infallible. Along with the new technology and new capabilities comes a new set of concerns to be aware of. Marketers need to ask a lot of questions — about everything from the motivations of the company selling the AI, to where the training data is coming from. […]

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