Perfect detection of computer-generated text faces fundamental challenges
Source abstract
Abstract
Recent advancements in large language models (LLMs) have sparked a debate on the detection of artificial intelligence (AI)-generated text, a concern especially prevalent among academic institutions and publishers. While current detection tools claim high accuracy rates, some studies point to their unreliability. This paper contends that efforts to detect AI writing are fundamentally flawed because improved detection capabilities could inadvertently refine AI writing tools, leading to a technological arms race. Moreover, the rapid evolution of LLMs means detection methods may quickly become obsolete. We propose a focus on ethical guidelines rather than outright prohibitions, emphasizing that technological solutions should complement, not replace, the core ethical principles of scientific publishing.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Artificial Intelligence in Healthcare and Education · Explainable Artificial Intelligence (XAI) · Ethics and Social Impacts of AI
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (14)
Show source-provided references
1. Tools such as ChatGPT threaten transparent science; here are our ground rules for their use
2. Artificial Intelligence Can Generate Fraudulent but Authentic-Looking Scientific Medical Articles: Pandora’s Box Has Been Opened
3. 2022
4. Distinguishing academic science writing from humans or ChatGPT with over 99% accuracy using off-the-shelf machine learning tools
5. Testing of detection tools for AI-generated text
6. Inferring algorithmic patterns with stack-augmented recurrent nets
7. AI vs. Human-Differentiation Analysis of Scientific Content Generation
8. Generative adversarial nets
9. On the Reliability of Watermarks for Large Language Models
10. Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense
11. RADAR: Robust AI-Text Detection via Adversarial Learning
12. On the Origin of LLMs: An Evolutionary Tree and Graph for 15,821 Large Language Models
13. Generative AI models should include detection mechanisms as a condition for public release10.1007/s10676-023-09728-4 ↗
14. AI Is Changing the Landscape of Academic Writing: What Can Be Done? Authors’ Reply to: AI Increases the Pressure to Overhaul the Scientific Peer Review Process. Comment on “Artificial Intelligence Can Generate Fraudulent but Authentic-Looking Scientific M