# : “I’m sorry, Dave, I’m afraid I can’t do that” Part 2

## TL;DR

This paper discusses the development of AI tools to detect AI-generated text and explores ethical and practical implications for academic publishing.

## Contribution

The paper reviews existing AI text detection tools and raises questions about their reliability and ethical use in scholarly work.

## Key findings

- AI detection tools have been developed and tested for identifying AI-generated text.
- The effectiveness of these tools varies, raising concerns about reliability and accuracy.
- The paper questions how AI-generated text can be used responsibly in academic and professional writing.

## Abstract

In the WYPT session at the Baltimore ACA meeting in 2023, I instigated a discussion on the use/abuse/future of AI generated text in publications (and elsewhere). One of the main take-aways from that talk was the prospect of AI detecting itself: that is, AI applications that can detect with some level of accuracy and precision whether a piece of text was generated by human or AI. Since then, there have been such routines developed and tested. One (of many) report did a fairly thorough analysis of the most common software: (W.H. Walters, “The Effectiveness of Software Designed to Detect AI-Generated Writing: A Comparison of 16 AI Text Detectors” https://doi.org/10.1515/opis-2022-0158 )

This talk will present some conclusions from that report and pose some questions on how to proceed: Can there be safeguards to distinguish AI text reliably? Can these contribute to defining potential legitimate uses of AI-generated text while still protecting copyright and IP? To what extent might we be able to use AI-generated text to prepare publications, reviews, grant proposals, etc.

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Source: https://tomesphere.com/paper/PMC12585750