Ethical horizons in generative AI: addressing bias and advancing fairness through responsible frameworks

Purpose This study aims to explore the ethical dilemmas posed by generative artificial intelligence (AI) and frames a responsible development framework for AI. It studies influential factors in ethical AI development. The primary study further discusses concerns about data privacy, autonomy and accountability in the context of generative AI systems. Design/methodology/approach The suggested framework then relies on gray influence analysis (GINA) to assess the degree of influence, which is essential to ethical AI development. It identifies eight key factors, which include transparency, accountability and human bias, as important for ethical AI development. The framework focuses on interventions through GINA to reduce bias and achieve equal AI systems. Findings This study shows that the most influential factor in ethical AI development is “Autonomy and human bias,” followed by “Intentionality and responsibility.” In contrast, the “Automation and replacement” factor was ranked the least influential. Research limitations/implications These factors are systematically considered, with stakeholders developing strategies to facilitate ethical AI development and societal welfare. Future research directions would include the necessary competencies and resources for controlling generative AI, studies of biases in training data sets and the identification of optimal contexts for the deployment of generative AI systems. Originality/value This study uniquely explores and identifies influential factors in ethical AI development to address bias and advance fairness.