AI Ethics and Bias in Image Generation

AI-generated images can inherit biases from training datasets, leading to ethical concerns regarding representation and inclusivity. Models trained on unbalanced datasets may reinforce stereotypes, affecting the diversity of generated images.

To address bias, developers implement dataset balancing techniques and adversarial debiasing strategies. Transparency in dataset sourcing and algorithmic adjustments help mitigate unwanted biases.

Regulatory frameworks are being explored to ensure ethical AI-generated content usage, particularly concerning deepfake images and synthetic media manipulation.