The key…is often to get more data from underrepresented groups. For example…an AI model was twice as likely to label women as low-income and men as high-income. By increasing the representation of women in the dataset by a factor of 10, the number of inaccurate results was reduced by 40 percent.

Source: MIT researchers show how to detect and address AI bias without loss in accuracy | VentureBeat

What many people don’t understand about algorithmic bias is that it’s corrected quite easily, relative to the challenge of correcting bias in human beings. If machine learning outputs are biased, we can change the algorithm, and we can change the datasets. What’s the plan for changing human bias?

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