Generative AI and Ethical Imperative: Confronting Bias in Content Creation using Random Forest

Generative AI has completely changed content creation, as it empowered the generation of diverse media that could be in text, images, or even audio. As these technologies roll out in different sectors - from entertainment to healthcare and education-they bring with them deep-seated ethical concerns, particularly those around issues of bias. The paper addresses the intrinsic biases which come from the training datasets, reflecting existing prejudices in society. Such biases might have devastating implications leading to stereotype enforcement and persistent inequality. We explore the various types of bias introduced by generative AI and the ethical implications of such bias when applied to practical usage scenarios. Additionally, we review the contemporary frameworks and methods that diminish bias, for instance FairGAN and FairGAN+ which introduce fairness constraints during the generative process. The work scrutinizes the progressions to raise a big thumbs up for ethical practice in developing and deploying generative AI technologies towards providing more balanced and responsible AI systems.