To prevent algorithmic bias, the authors call for multivariable modeling frameworks that jointly incorporate biological sex, genetic ancestry, and gender-related life-course exposures.
Who gets the job interview. Who receives public benefits. Who is flagged as high risk. Increasingly, these outcomes are shaped not by human deliberation but by algorithmic systems embedded deep within ...
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