On the Role of Artificial Intelligence in Human-Machine Symbiosis

2026-05-01

AI Summary

This work addresses the challenge of identifying the functional roles assumed by AI systems—such as assistive editing or creative generation—from isolated generated texts without access to dialogue context, a limitation that impedes ethical evaluation of AI use in terms of fairness, transparency, and appropriateness. To overcome this, the authors propose a novel method that infers the AI’s latent role from the input prompt, explicitly embeds this role during text generation, and subsequently reconstructs the nature of AI involvement from the output alone. This approach enables, for the first time, automatic identification of functional roles in standalone AI-generated texts, demonstrating robustness against perturbations while preserving high linguistic quality. The method provides an interpretable technical foundation for auditing AI systems from an ethical standpoint.

Abstract

The evolution of artificial intelligence (AI) has rendered the boundary between humanity and computational machinery increasingly ambiguous. In the presence of more interwoven relationships within human-machine symbiosis, the very notion of AI-generated information becomes difficult to define, as such information arises not from either humans or machines in isolation, but from their mutual shaping. Therefore, a more pertinent question lies not merely in whether AI has participated, but in how it has participated. In general, the role assumed by AI is often specified, either implicitly or explicitly, in the input prompt, yet becomes less apparent or altogether unobservable when the generated content alone is available. Once detached from the dialogue context, the functional role may no longer be traceable. This study considers the problem of tracing the functional role played by AI in natural language generation. A methodology is proposed to infer the latent role specified by the prompt, embed this role into the content during the probabilistic generation process and subsequently recover the nature of AI participation from the resulting text. Experimentation is conducted under a representative scenario in which AI acts either as an assistive agent that edits human-written content or as a creative agent that generates new content from a brief concept. The experimental results support the validity of the proposed methodology in terms of discrimination between roles, robustness against perturbations and preservation of linguistic quality. We envision that this study may contribute to future research on the ethics of AI with regard to whether AI has been used fairly, transparently and appropriately.

Problem

• human-machine symbiosis
• AI role tracing
• natural language generation
• functional role
• AI participation

Innovation

• AI role tracing
• human-machine symbiosis
• natural language generation
• latent role embedding
• AI participation inference