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

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\u27s 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