Contextual Prompt Engineering for Enhanced Ethical AI Outputs: Strategies for Bias Mitigation and Transparent AI Responses

Abstract

With AI models influencing decisions across industries, addressing bias and ensuring transparency is critical. This study explores advanced prompt engineering techniques to foster ethical AI responses by reducing biases and enhancing transparency. A context-sensitive framework is developed to guide practitioners in crafting prompts tailored to ethically sensitive domains such as healthcare, media, and business. By leveraging contextual clues, the framework promotes fair representation and social responsibility. Case studies and practical examples illustrate its effectiveness in producing unbiased outputs and exposing model limitations. Iterative refinement strategies further enable AI models to disclose uncertainties, fostering trust. This research advances ethical AI by embedding fairness, transparency, and accountability into AI systems, offering actionable insights for practitioners and policymakers.