ArXiv TLDR

On the Role of Artificial Intelligence in Human-Machine Symbiosis

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2605.00440

Ching-Chun Chang, Yuchen Guo, Hanrui Wang, Timo Spinde, Isao Echizen

cs.AIcs.CLcs.HC

TLDR

This paper proposes a method to trace the functional role of AI in natural language generation, even when the original prompt is unavailable.

Key contributions

  • Tackles the problem of tracing AI's specific functional role in human-machine generated content.
  • Introduces a methodology to infer, embed, and recover AI's latent role from natural language generation.
  • Validated the methodology by distinguishing AI as an assistive editor or a creative generator.

Why it matters

This research is crucial for understanding AI's participation in content creation, especially as human-machine symbiosis grows. It lays groundwork for future ethical considerations regarding fair, transparent, and appropriate AI use.

Original 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.

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