'Your AI Text is not Mine': Redefining and Evaluating AI-generated Text Detection under Realistic Assumptions
arXiv:2606.04906v1 Announce Type: cross Abstract: Although it is generally agreed that AI-generated text poses a broad societal risk, there is no common understanding in the AI-generated text detection literature on what constitutes harmful use. Rather, existing datasets and approaches often define their own criteria and make their own assumptions, sometimes implicitly, and often only loosely related to real-world needs and applications. To address this gap, we here systematically define various notions of AI-generated text and their characteristics. To study these, we collect AITDNA - a new b
The proliferation of advanced AI text generation models necessitates a more robust and realistic approach to detection to address real-world risks.
A common understanding and standardized evaluation of AI-generated text detection are crucial for policymakers, industry, and the public to manage societal and informational integrity risks.
This research introduces a systematic framework and new dataset for evaluating AI text detection, moving beyond ad-hoc criteria towards more practical and consistent approaches.
- · AI ethicists
- · Content verification platforms
- · Academic researchers
- · Regulators
- · Malicious actors using AI for disinformation
- · Generative AI platforms without robust provenance tools
Improved detection methods could lead to more effective strategies against AI-driven disinformation and fraud.
Standardized evaluation could drive innovation in AI-generated text detection technologies and foster greater trust in information.
The definition of what constitutes harmful AI-generated text may influence future legal frameworks and platform policies regarding AI content.
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Read at arXiv cs.AI