Cutting out clustered data points to reduce facial recognition bias explained in AFRL talk

Despite the presence of many “heavy hitters” in the public discourse about bias in facial recognition, misunderstanding about the nature of the problem and where it comes from is widespread, according to a recent talk as part of the Applied Face Recognition Lab’s virtual talk series.

John Howard, principal data scientist of the Identity and Data Sciences Laboratory at the Maryland Test Facility, delved into the issue in a presentation titled ‘Understanding and Mitigating Bias in Human & Machine …

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