Trust Dynamics in the Digital Economy: Ethical AI and Data Governance Frameworks

This study presents a fuzzy logic framework for modelling trust dynamics and ethical decision-making in artificial intelligence (AI) systems within the digital economy. The research investigates how trust, a crucial component of AI adoption, evolves based on adherence to ethical principles, such as privacy, fairness, and transparency. A fuzzy rule-based system was developed to quantify trust as a dynamic, context-dependent variable, rather than a binary concept. The model incorporates fuzzy membership functions for key ethical dimensions, with privacy, fairness, and transparency being treated as fuzzy variables. The study further analyzes how varying adherence levels to these ethical principles impact the overall trust score of AI systems. The results show that trust increases incrementally based on an AI system’s ethical behaviour. High adherence to privacy, fairness, and transparency results in higher trust scores, with privacy being the most influential factor. A sensitivity analysis demonstrates that reduced privacy significantly lowers trust, indicating the importance of data governance in AI trust dynamics. The fuzzy logic framework effectively models trust as a continuum, addressing the complexities of ethical decision-making in AI systems. This approach allows for adaptable, context-aware ethical decision-making, emphasizing the importance of privacy in data governance and providing practical insights for developers, policymakers, and organizations aiming to build trustworthy AI systems. Future research could integrate machine learning to dynamically adjust ethical dimensions, further refining the trust model.