Data dies in darkness: getting AI algorithms to think outside the black box paid Kinetica
If they are trained to make decisions using a data set that doesn’t include enough examples or biased examples of certain groups of people, they will produce unintentionally biased results—or worse, decisions, asalgorithm trained using that collection of image data
The parameters of what the algorithm should take into account when analyzing a data set are still set by people. And the developers and data scientists doing this work may not be aware of the unconscious biases the parameters they’ve put in place contain. We don’t know what the parameters were for the Apple Card’s credit determinations, but if factors included annual income without considering joint property ownership and tax filings, women, who in America still make 80.
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