Generative AI models have shown impressive results in generating novel and diverse outputs, but they can perpetuate biases present in the training data, leading to unfair outcomes. Demographic bias and stereotyping bias are two types of bias that can occur, and fairness metrics such as demographic parity and equalized odds have been developed to quantify and mitigate these biases. Techniques like data augmentation, regularization, and adversarial training have been proposed to debias generative AI models. Future research directions include developing more advanced generative models, integrating generative AI with other areas of AI, and exploring its applications in healthcare and creative industries while addressing potential risks and challenges.
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