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When AI is used for changing an image or video file, such as cropping it automatically, any bias built in can adapt that image unfavourably. Cropping, for example, may favour lighter skinned people and reduce the visibility of darker skinned people. This gives a false impression of reality through an apparent reality-based medium (seeing is believing).
Tagging media files with descriptive keywords and search terms also impacts how they are displayed in search results, and can mislead if tags used to retrieve an image are unfavourable or prejudiced. All the above relate to forms of media file manipulation or processing.
Another reminder that bias, testing, diversity is needed in machine learning: Twitter’s image-crop AI may favour white men, women’s chestsStrange, it didn’t show up during development, says social network Digital…
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Gender Shades: Intersectional Accuracy Disparities in Commercial Gender ClassificationRecent studies demonstrate that machine learning algorithms can discriminate based on classes like race and gender. In this work, we present an…
Once again, racial biases show up in AI image databases, this time turning Barack Obama whiteResearchers used a pre-trained off-the-shelf model from Nvidia. Digital imagery tagging provides negative context for…
We and AI Ltd, a Charitable Company Limited by Guarantee Company Registration Number 13376771
