AIMED: Towards a Philosophically Legitimated AI-assisted Iterative Method for Ethical Deliberation

This paper addresses the accelerating crisis of ethical governance in an age of complex socio-technical change, particularly in the domain of Artificial Intelligence. It poses a foundational philosophical question: when, if ever, is AI assistance in ethical deliberation legitimate? An answer is developed through three theses: i) the Ethical No-Free-Lunch (ENFL) principle, which establishes the indispensability of human normative intervention and accountability; ii) the Discovery/Justification Separation inspired by Reichenbach's work, which restricts AI use to the exploratory "context of discovery"; iii) the Algorithmic Mediated Control Framework (AMCF), which mandates that only scrutable, human-vetted deterministic algorithms generated with AI assistance, and not the AI itself, be entrusted with critical societal processes. From these theses, five legitimacy criteria for AI-assisted ethical deliberation are derived. Finally, the paper proposes the "AI-assisted Iterative Method for Ethical Deliberation" (AIMED), an actionable multi-stage workflow that fulfills the exposed criteria for ethical AI-assisted deliberation. This method integrates digital literature analysis, structured human–AI dialogue, human-only verification, and continuous feedback. The paper explicitly addresses several potential objections. It is shown how the AIMED framework aligns with and provides a concrete implementation for major international regulatory guidelines, such as the EU AI Act and the NIST AI Risk Management Framework. By situating the AIMED within traditions of proceduralism, the governance of inductive risk, and human–AI collaboration, the paper argues that this framework offers a philosophically justified, practically implementable model of AI-assisted ethical governance, that can be seen as an actionable instance of Digital Humanism.