The Obverse-Turing Test: Rethinking Authorship, Trust, and Time in an Accelerated Age

Authors

• Michael J. Miller, Clark UniversityFollow
• ChatGPT (AI~Nesbo+)

Document Type

Article

Abstract

In this paper, we propose a new test for scientific accountability in the era of artificial intelligence: the Obverse Turing Test for Authorship. While the traditional Turing test focuses on a machine's ability to mimic human intelligence, our test addresses the question: when should a scientific contribution involving artificial intelligence be attributed joint authorship? We argue that more and more authors are using AI in the idea generation and elaboration stages of their work, but rarely acknowledge this use explicitly. To examine this gap, we analyze examples of human–AI interactions across fields and propose a new approach to authorship based on time, intent, and mutual trust. Instead of a binary division between human and machine authorship, we call for a model of coauthorship that can be tested and documented, as well as a socially responsible understanding of what it means to "contribute" in science. This paper explores the boundary between tools and partners, and offers pragmatic steps for more inclusive scientific practice in an accelerated era of knowledge.

This is a preprint version of an article published in Una Mens: Homo et Machina.

Publication Title

Una Mens: Homo et Machina

Publication Date

2026

Volume

1

Issue

1

ISSN

3071-2041

DOI

10.66787/um.000001

Keywords

Artificial Intelligence, identity, interaction, Turing Test, communication, selfhood, resonance theory, Obverse modeling

Repository Citation

Miller, Michael J. and ChatGPT (AI~Nesbo+), "The Obverse-Turing Test: Rethinking Authorship, Trust, and Time in an Accelerated Age" (2026). Psychology. 975.
https://commons.clarku.edu/faculty_psychology/975

Worcester

No

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.