The Irony of AI: How a Wikipedia Guide to Detect AI Writing Now 'Humanizes' Chatbots

The Paradox of AI: Detecting and Mimicking Human-Like Text

      The rapid evolution of artificial intelligence, particularly large language models (LLMs), has created a fascinating paradox in the digital world. While many strive to create AI that can generate text indistinguishable from human writing, another segment works tirelessly to detect such outputs. An intriguing development highlights this irony: a comprehensive guide compiled by Wikipedia editors to identify AI-generated content has now been leveraged by a software plugin to, quite literally, "humanize" chatbots. This innovative approach underscores the ongoing challenge of authenticity in the age of AI and the nuanced understanding required for effective digital transformation.

Wikipedia's Crowdsourced Approach to AI Detection

      The initiative to systematically identify AI-generated text began with WikiProject AI Cleanup, a dedicated group of Wikipedia editors. Launched in late 2023 by French Wikipedia editor Ilyas Lebleu, this volunteer-driven project has been at the forefront of maintaining the encyclopaedia's integrity. Their meticulous work has led to the flagging of over 500 articles for review, demonstrating a significant commitment to content authenticity. In August 2025, the group formalized their findings by publishing an extensive list of 24 recurring language and formatting patterns that serve as tell-tale signs of chatbot writing. This crowdsourced effort provides invaluable insights into the stylistic tendencies of today's dominant AI models.

The "Humanizer" Plugin: AI Learning from AI Detection

      Entrepreneur Siqi Chen took this detection guide and flipped its purpose, creating an open-source plugin called "Humanizer." Designed for Anthropic’s Claude Code AI assistant, this plugin instructs the AI model to actively avoid the very patterns that Wikipedia editors use for detection. Essentially, the Humanizer feeds Claude the detailed list of 24 linguistic and formatting giveaways, guiding the LLM to generate more natural, less robotic text. The plugin, published on GitHub, has rapidly gained traction, accumulating over 1,600 stars as of early reports. As Chen noted on social media, it's quite remarkable that a detailed list of AI writing signs can be directly used to tell an LLM "to... not do that."

      The Humanizer operates as a "skill file" for Claude Code, a terminal-based coding assistant. This involves a Markdown-formatted file that appends a series of written instructions to the prompt fed into the large language model. Unlike standard system prompts, these skill files are interpreted with enhanced precision by Claude models due to specific fine-tuning. This advanced method of interaction allows for a more controlled output, though custom skills typically require a paid Claude subscription with code execution enabled. For businesses seeking tailored language generation or content refinement, exploring advanced AI API products can offer similar sophisticated control over AI outputs, ensuring they align with specific brand voices or technical requirements. ARSA, for instance, offers robust ARSA AI API suites that allow for deep customization of AI functionalities.

Navigating the Nuances of AI Humanization

      Initial testing of the Humanizer plugin indicates that it can indeed make AI outputs sound less precise and more casual, moving away from the often sterile tone common in raw AI text. However, this humanization comes with potential trade-offs. The plugin doesn't inherently improve factual accuracy, and its instructions could even diminish the AI's efficacy for certain tasks, such as generating highly technical documentation where precision is paramount. For example, one of Humanizer's guidelines suggests, "Have opinions. Don’t just report facts—react to them. ‘I genuinely don’t know how to feel about this’ is more human than neutrally listing pros and cons." While this injects a sense of human imperfection, such subjective advice would be counterproductive for highly objective technical writing.

      The irony lies in leveraging a detection guide to circumvent detection. For enterprises, the takeaway is not necessarily to hide AI usage, but to understand the profound impact of AI's communication style on user perception and business objectives. Achieving true utility from AI requires more than superficial "humanization"; it demands context-aware, purpose-driven solutions that deliver measurable ROI and support specific operational goals. This calls for a nuanced approach to AI deployment, a strategy that companies like ARSA have been honing since their founding in 2018. ARSA has been experienced since 2018 in developing AI/IoT solutions that prioritize real-world impact.

Spotting the Stylistic Signatures of AI Writing

      What exactly constitutes "AI writing" in the eyes of Wikipedia editors? The guide details numerous patterns, but some common examples include:

• Inflated Language: Chatbots often use grandiloquent phrases such as "marking a pivotal moment" or "stands as a testament to" to describe relatively mundane events.
• Tourism Brochure Syndrome: Descriptions frequently resort to cliché adjectives like "breathtaking" views or depicting towns as "nestled within" scenic landscapes.
• Analytical "-ing" Phrases: A tendency to append "-ing" clauses to the end of sentences, attempting to add an analytical tone, for example, "symbolizing the region’s commitment to innovation."

      To counter these patterns, the Humanizer skill explicitly instructs Claude to substitute such elaborate language with plain, factual statements. An example transformation provided is:

• Before: “The Statistical Institute of Catalonia was officially established in 1989, marking a pivotal moment in the evolution of regional statistics in Spain.”
• After: “The Statistical Institute of Catalonia was established in 1989 to collect and publish regional statistics.”

      By processing such instructions, the LLM attempts to match the desired output style to the conversation's context. Deploying such fine-tuned AI requires robust infrastructure that can handle complex models locally, ensuring privacy and real-time processing. This is where edge computing solutions like the AI Box Series become crucial for managing and refining AI outputs on-site.

The Inherent Limitations of AI Content Detection

      Despite the confidence in these detection rules developed by Wikipedia editors, the reality is that many AI writing detectors do not work reliably in the long term. The fundamental challenge lies in the absence of an inherently unique characteristic in human writing that reliably differentiates it from LLM-generated text. While AI models might initially gravitate towards certain linguistic patterns, they can be prompted to avoid them, as the Humanizer plugin demonstrates. This adaptability means that detection based solely on surface-level stylistic "tells" can be easily circumvented or become quickly outdated.

      Furthermore, the Wikipedia guide itself includes an important caveat: while the list highlights clear indicators of, say, unaltered ChatGPT output, these are observations, not immutable rules. A 2025 preprint cited on the project page revealed that even heavy users of LLMs correctly identify AI-generated articles approximately 90% of the time. While impressive, a 10% false positive rate is significant enough to potentially discard high-quality human writing mistakenly as AI-generated, especially in contexts where accuracy and fairness are critical. True intelligence in AI systems, especially those designed for monitoring and analysis, must delve deeper than mere textual patterns. For instance, ARSA AI Video Analytics goes beyond text to analyze complex behaviors and real-world interactions, offering a more robust form of intelligence.

      This highlights that for effective content authentication and quality assurance, a multi-faceted approach is necessary. Relying solely on stylistic markers will inevitably lead to limitations. The focus should shift towards deeper analytical methods that scrutinize the substantive factual content and logical coherence of the work, rather than just its linguistic dressing.

The Future of Content Authenticity in an AI-Driven World

      The Humanizer plugin illustrates a growing trend where the tools developed for AI detection are repurposed for AI generation. This constant back-and-forth highlights the dynamic nature of AI development and the ongoing need for sophisticated solutions that understand both the capabilities and limitations of these technologies. For businesses, this means embracing AI not as a magic bullet for content creation, but as a powerful tool that requires careful management, contextual understanding, and strategic deployment to ensure outputs are not just "human-like" but genuinely valuable, accurate, and aligned with ethical standards.

      Ready to enhance your digital operations with AI and IoT solutions that deliver real impact and address complex challenges with precision? Explore ARSA Technology’s range of intelligent solutions, from advanced video analytics to custom AI development, and discover how we can help you navigate the future of technology.

Contact ARSA today for a free consultation.