Consider factors like objectives of the company selling the AI, training data and personal interpretations.
Marketers need to devise clear methods to ensure that AI algorithms, particularly machine learning tools, are free of bias. When marketers have better or more data on certain situations or customers, bias can occur, resulting in algorithms that favour data sets with more information.
Excessive volumes of data about existing customers, algorithms trained to discover lower-value customers, are examples of biased data sets. To eliminate bias, include more human decisions, identify differences and patterns, and ensure the training data accurately represents the target demographic.
Predict unexpected results, like conversions, and ensure they're "over-indexed" in training data. Evaluate subgroups of metrics like platforms, genders, and more, and update training data frequently. Account for algorithmic bias in AI-driven solutions, before investing in them.
[4 minute read]
