The notion that bias in AI models is solely caused by biased data is a common misconception. While biased data undoubtedly contributes to AI bias, algorithmic choices and engineering decisions also play a significant role in perpetuating systemic inequalities and amplifying existing biases.
When we talk about biased data, we often refer to the concept of "garbage in, garbage out." This means that if the data used to train an AI model contains inherent biases, such as racial or gender disparities, the model will learn to replicate those biases. For example, facial recognition algorithms have been shown to misidentify people of color at a higher rate than white individuals.
However, even if the data is carefully curated and accurate, algorithmic choices and engineering decisions can still introduce bias. For instance, the choice of which features to include in a model can skew the results towards a particular group. In a study on credit scoring models, researchers found that models ...
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