This paper investigates the extent to which DALL-E Mini, a generative AI image model, reflects gender and racial biases present in society. The authors analyze the images generated by DALL-E Mini to assess whether they reinforce stereotypes about occupations, such as portraying certain jobs as predominantly male or female, or predominantly white. They find that the images tend to represent many occupations as being populated either solely by men or solely by women, and predominantly by white people, with very few non-white individuals. These findings suggest that AI technologies like DALL-E Mini may perpetuate and amplify existing social biases. The authors emphasize the need for critical scrutiny and regulation of such technologies before they are widely adopted. They also highlight the importance of understanding the biases embedded in generative AI systems, as these biases can influence human decisions and reinforce societal inequalities. The study underscores the need for further research into the biases present in generative AI and the development of methods to mitigate them. The authors conclude that while generative AI has the potential to revolutionize various fields, it is crucial to address the biases that may be embedded in these systems to ensure they are used ethically and fairly.This paper investigates the extent to which DALL-E Mini, a generative AI image model, reflects gender and racial biases present in society. The authors analyze the images generated by DALL-E Mini to assess whether they reinforce stereotypes about occupations, such as portraying certain jobs as predominantly male or female, or predominantly white. They find that the images tend to represent many occupations as being populated either solely by men or solely by women, and predominantly by white people, with very few non-white individuals. These findings suggest that AI technologies like DALL-E Mini may perpetuate and amplify existing social biases. The authors emphasize the need for critical scrutiny and regulation of such technologies before they are widely adopted. They also highlight the importance of understanding the biases embedded in generative AI systems, as these biases can influence human decisions and reinforce societal inequalities. The study underscores the need for further research into the biases present in generative AI and the development of methods to mitigate them. The authors conclude that while generative AI has the potential to revolutionize various fields, it is crucial to address the biases that may be embedded in these systems to ensure they are used ethically and fairly.