Human-machine interactions research needs to include diverse subjects, regardless of ability, ethnicity or social class, and this research should provide benefit for all people, argue the authors of a new SciencePerspective. Read more:
). In the case of facial recognition algorithms, the pool of training data may be heavily biased toward one race, which would lead the algorithm to make false predictions when it encounters other races.
Another aspect of culturally aware HMI is to recruit diverse students to engage in this research who can also serve as ambassadors to diverse communities. Despite the great pressures to publish research quickly, constant reliance on convenience samples for datasets is problematic and exclusionary. Researchers could recruit and train students as early as high school on best practices in human-subject studies and partner with them to reach diverse communities.
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