Henriette Cramer, Jean Garcia-Gathright, Aaron Springer, Sravana Reddy
Unfair algorithmic biases and unintended negative side effects of machine learning (ML) are gaining attention—and rightly so. We know that machine translation systems trained on existing historical data can make unfortunate errors that perpetuate gender stereotypes. We know that voice devices underperform for users with certain accents, since speech recognition isn't necessarily trained on all speaker dialects. We know that recommendations can amplify existing inequalities. However, pragmatic challenges stand in the way of practitioners committed to addressing these issues. There are no clear guidelines or industry-standard processes that can be readily applied in practice on what biases to assess…
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