AI bias
Also called algorithmic bias
When an AI system's outputs systematically favour or disadvantage some people or views, usually because of patterns in its training data.
A model learns from data made by people, and that data carries people's patterns: who appears in photos, whose writing dominates the web, which groups past decisions favoured. The model reproduces those patterns and can sharpen them, because it leans towards what was most common.
This has been measured for years. An early study of commercial face-analysis systems found error rates of up to 34.7% for darker-skinned women against at most 0.8% for lighter-skinned men. Newer systems show it too. Bias can also come from design choices, like which answers raters rewarded, not only from the data.
For example
In a test of 2 leading image generators, prompts that didn't mention skin colour produced light-skinned people in more than 96% of 3,200 images.
Put it to use
Where you'll meet AI bias in ChatGPT, Gemini and Claude, how to use it there, and the mix-up most people make.
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