Ethics and Technical Aspects of Generative AI Models in Digital Content Creation
It addresses ethical and technical issues for industries using generative AI, proposing guidelines for responsible integration, but is incremental as it reviews existing models without introducing new methods.
This paper examines the performance and ethical challenges of generative AI models like GPT-4o and DALL-E 3 in digital content creation, finding they enhance creativity and efficiency but often reflect biases from training data and pose ethical risks.
Generative AI models like GPT-4o and DALL-E 3 are reshaping digital content creation, offering industries tools to generate diverse and sophisticated text and images with remarkable creativity and efficiency. This paper examines both the capabilities and challenges of these models within creative workflows. While they deliver high performance in generating content with creativity, diversity, and technical precision, they also raise significant ethical concerns. Our study addresses two key research questions: (a) how these models perform in terms of creativity, diversity, accuracy, and computational efficiency, and (b) the ethical risks they present, particularly concerning bias, authenticity, and potential misuse. Through a structured series of experiments, we analyze their technical performance and assess the ethical implications of their outputs, revealing that although generative models enhance creative processes, they often reflect biases from their training data and carry ethical vulnerabilities that require careful oversight. This research proposes ethical guidelines to support responsible AI integration into industry practices, fostering a balance between innovation and ethical integrity.