Under What Conditions Can a Machine Become Genuinely Creative?

2026-06-11

AI Summary

This study addresses the conditions under which machines can achieve genuine creativity and how such creativity can safeguard human agency within human-machine symbiosis. Grounded in Designics theory, the work defines creativity as a structural, recursive transformation of incomplete situations and articulates ten necessary conditions spanning three dimensions: perception, conflict, and capability. Departing from conventional evaluation paradigms centered on output novelty, it uniquely internalizes value orientation and human-machine cohabitation as core constituents of machine creativity. The computational feasibility of this framework is demonstrated through recursive element extraction, autonomous grid generation, and analyses of neurophysiological responses and cognitive workload. Empirical validation across multiple cyber-physical and cyber-biological case studies reveals that, despite their strong generative capacities, current generative systems still fail to fulfill all requisite conditions for authentic creativity.

Abstract

Recent AI systems can generate texts, software architectures, hypotheses, designs, and scientific workflows that appear creative. This paper asks under what conditions a machine can become genuinely creative, and how human agency can be preserved within shared cognitive and creative environments. It develops a requirement framework derived from Designics, the science of meaning-bearing intentional change. The paper argues that genuine machine creativity should not be defined by output novelty, current performance, or transient architecture alone. Instead, creativity is understood as the structural transformation of incomplete situations through recursive intervention dynamics. On this view, it depends on ten requirements: environment representation, scoped perception, conflict identification, intervention capability, consequence observation, knowledge and environment update, rescoping, local-to-global unfolding, value-based scoping, and human-AI co-living. These are organized through the three laws of Designics: perception, conflict, and capability. The paper illustrates the computational tractability of these requirements through selected cyber-physical and cyber-biological studies, including recursive element extraction, autonomous mesh generation, and neurophysiological and workload analysis. It then treats open-ended systems, automated discovery frameworks, self-modifying agents, foundation models, and agentic workflows as pressure cases: they demonstrate powerful generative means but do not by themselves establish genuine machine creativity. Finally, the paper argues that proactive AI ethics is internal to genuine machine creativity rather than an after-the-fact filter. Value-based scoping and human-AI co-living must shape how creative machines perceive environments, identify conflicts, select interventions, observe consequences, update knowledge, and rescope future action.

Problem

• machine creativity
• human agency
• Designics
• value-based scoping
• human-AI co-living

Innovation

• Designics
• recursive intervention dynamics
• value-based scoping
• human-AI co-living
• genuine machine creativity