SCENARIO-BASED APPROACHES TO EXPLAINABLE AI CODE GENERATION: BRIDGING TRANSPARENCY AND USABILITY

Abstract

As artificial intelligence (AI) becomes more integrated into critical applications, transparency and explainability remain key concerns, particularly for generative models. Despite their transformative capabilities, these models often act as “black boxes,” confusing decision-making processes from users and stakeholders. This paper explores how scenario-based design can be leveraged to increase AI code transparency, providing a structured approach to making generative models more explainable and accountable. This research aims to bridge the gap between complex AI systems and human understanding by analyzing the existing transparency challenges and reviewing state-of-the-art interpretability methods in the proposed scenario-driven strategies.Experiential case studies demonstrate the effectiveness of scenario-based interventions in improving model interpretability, fostering trust, and ensuring ethical AI deployment. Additionally, this study evaluates the effects of scenario-based design on model debugging, bias detection, and user accessibility, providing insights into how transparency initiatives can mitigate algorithmic risks.The conclusion highlights the need for interdisciplinary collaboration among AI researchers, designers, and policymakers to develop a strong framework for transparent generative AI. By integrating structured explanatory techniques, this paper contributes to ongoing efforts in responsible AI development and provides a roadmap for future developments in this field.