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How should product builders think about data bias in AI systems?

AI ProductBiasMeasurement

Drawn from Lutz Finger's Forbes column, LinkedIn writing, and Cornell teaching. Sources are cited inline so you can read the originals.

Bias in data is inevitable; product decisions determine how much to accept.

There is never a way to have non-biased data, because data is biased. As a product person, you need to decide by how much you want to de-bias the data. Sometimes bias is actually good because you don’t want averages, you want the best outcome. For example, if identifying certain patients or optimal machinery output, you don’t want what the machine did in the past all the time, but just the best parts of it. You’re injecting very good data versus just average data.

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