English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Many existing works have made great strides towards reducing racial bias inface recognition. However, most of these methods attempt to rectify bias thatmanifests in models during training instead of directly addressing a majorsource of the bias, the dataset itself. Exceptions to this areBUPT-Balancedface/RFW and Fairface, but these works assume that primarilytraining on a single race or not racially balancing the dataset are inherentlydisadvantageous. We demonstrate that these assumptions are not necessarilyvalid. In our experiments, training on only African faces induced less biasthan training on a balanced distribution of faces and distributions skewed toinclude more African faces produced more equitable models. We additionallynotice that adding more images of existing identities to a dataset in place ofadding new identities can lead to accuracy boosts across racial categories. Ourcode is available athttps://github.com/j-alex-hanson/rethinking-race-face-datasets.

Rethinking Common Assumptions to Mitigate Racial Bias in Face Recognition Datasets