Toward an artifact that designs itself: generative design science research approach

Abstract The rapid advancement of artificial intelligence (AI) has introduced profound societal and ethical challenges, necessitating a paradigm shift in AI system design. This paper introduces a novel framework that enables AI systems to design, audit, and evolve themselves ethically, through an adaptation of the echeloned design science research (eDSR) methodology. These AI systems will certainly evolve beyond mere tools to design, refine, and govern themselves within ethical constraints. The framework embeds four core principles: responsible autonomy, where AI systems self-regulate their decisions within ethical boundaries; AI self-explainability, enabling AI-to-AI transparency and internal decision auditing; AI bootstrapping, supporting iterative self-enhancement; and knowledge-informed machine learning (KIML), which integrates domain expertise for context-aware learning. We extend the concept of AI-as-a-User-of-AI, wherein autonomous AI agents behave as collaborative entities that engage in structured dialogues to refine decisions and enforce ethical alignment . Unlike traditional systems that rely on human-in-the-loop oversight or post-hoc explanations, our framework allows AI to monitor and evolve its reasoning in real time. By embedding ethical reasoning, self-explanation, and learning directly into system architecture through modular design echelons, the proposed generative eDSR (GeDSR) framework combines eDSR’s structured and multi-phased approach with AI-to-AI collaboration, which enables scalability, adaptability, and ethical alignment across diverse applications. By embedding ethical reasoning and iterative learning at the architectural level, the proposed framework promotes the development of self-improving AI systems aligned with human values, thus laying the groundwork for a shift from human-dependent oversight to a resilient, AI-centric ecosystem.