Commercial AI facial recognition systems tend to misclassify darker-skinned females more than any other group (lighter-skinned males being the most accurately classified).
Digital imagery tagging provides negative context for non white people.
New AI tools are trained to generate new written content, like articles or image descriptions. This new text reflects the language the AI has been trained on, and can mirror biases in the training data and text sources.
When AI is used for changing an image or video file, any bias built in can adapt that image unfavourably: favouring lighter skinned people and reducing the visibility of darker skinned people. This gives a false impression of reality through an apparent reality-based medium (seeing is believing).
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