A research team introduced SynthFaces, a balanced large-scale synthetic human face dataset intended to address the imbalanced identity representation and privacy risks of real-world datasets used in human-centric AI tasks. The dataset is positioned as an alternative to real-world data, which the researchers say offers imbalanced representations of identity and often contains sensitive personal information. The work targets fair and equitable AI by supplying balanced representations without sensitive personal data.
The pursuit of fair and equitable AI in human-centric tasks is critically hindered by the inherent biases and privacy risks of real-world datasets, which offer imbalanced representations of identity and often contain sensitive personal information. Consequently, state-of-the-art generative models tr
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