Demographic-prediction algorithms have various challenges, following best practices can minimize the harms.
Alternatively, you could test existing tools to see which ones perform well on specific subsets of a population. For example, although name-based algorithms do a poor job of distinguishing between US racial categories, they are fairly reliable at differentiating between ‘British origin’ and ‘East Asian’ names.
More generally, algorithm designers should release information on subgroup and cross-population performance to help users to better understand the validity and reliability of these tools. If providers of large data sets want to continue including imputed demographics, they need to take these factors into consideration and provide information on error rates by subgroups.
By avoiding individual predictions, researchers can sidestep some of the hazards of ascribing identities to people. And, they can attempt to correct for known biases. For example, our finding that one algorithm misgenders women at a higher rate than men means that it reports more men than exist in the population. Armed with this information, we could adjust the algorithm’s predictions accordingly.
France Dernières Nouvelles, France Actualités
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