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Bottom-up and major-down is how I take into consideration addressing bias in AI. Bottom-up involves very careful array of datasets and thinking of that has traditionally been disregarded of contributing to People datasets.

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making sure the check and validation details is diversified since they go on to really (statistically) depict the actual (generation) facts, and likewise ongoing retraining of ML models might help lessen the bias in design results.

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at the time we have determined These "voices," we can easily get the job done to dietary supplement our datasets with what is lacking. Top-down signifies creating rules to supervise and guarantee moral usage. - Lynne Sterrett, Deloitte

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