{"id":102304,"date":"2024-04-30T09:38:33","date_gmt":"2024-04-30T15:38:33","guid":{"rendered":"https:\/\/www.vendasta.com\/blog\/?p=102304"},"modified":"2026-01-12T04:13:07","modified_gmt":"2026-01-12T04:13:07","slug":"how-to-avoid-ai-detectors","status":"publish","type":"post","link":"https:\/\/www.vendasta.com\/blog\/how-to-avoid-ai-detectors\/","title":{"rendered":"How to Avoid AI Detectors with Genuine AI Content"},"content":{"rendered":"

While machine learning and <\/span>artificial intelligence<\/span><\/a> (AI) algorithms have been in development and use for several decades, their impact became unignorable when consumer-facing AI products like Midjourney and ChatGPT became available in 2022. These products showcased the capabilities and applications of AI models to the broader public, and since then, AI has been increasingly integrated into daily life for individuals and businesses.\u00a0<\/span><\/p>\n

As exciting as this new technology is, it also raises concerns about things like intellectual property and the authenticity of work. AI detector tools and services have emerged, promising to analyze content and detect output produced by AI models rather than humans. This leaves AI users wondering <\/span>how to avoid AI detectors<\/b>\u00a0and whether AI checkers are accurate at all. <\/span><\/p>\n

In this article, we\u2019ll take a look at where the evidence stands on detection tech for AI and whether it\u2019s possible to bypass AI detection. If you use AI in your agency work, these are important considerations to be aware of.<\/span><\/p>\n

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How accurate are AI detectors?<\/span><\/h2>\n

AI detectors can be accurate, but they have serious limitations and can\u2019t be relied upon to be 100% precise in their assessments. While a variety of tools have emerged making claims of high-accuracy AI detection\u2014some even assuring users of a nearly 100% accuracy rate\u2014many tools run into the issue of producing false positives. AI-generated content may accurately be identified, but human-generated content can still be interpreted as at least partially produced by AI.\u00a0<\/span><\/p>\n

For example, a user <\/span>tested<\/span> several detection tools including industry leader <\/span>WinstonAI<\/span><\/a>, which claims a 99.98% accuracy rate. While the AI-generated text was correctly flagged, the human-generated text was identified as 75% human. The AI checker was directionally correct in that it identified the text produced by ChatGPT 3.5 as AI-generated and the human-written text as <\/span>mostly<\/span><\/i> authentic.<\/span><\/p>\n

However, in situations when the stakes are high for identifying the authenticity of work, identifying human-produced text as 25% AI-generated can be a problem. That margin of error wouldn\u2019t be sufficient for, say, an educator to confidently penalize a student for submitting AI-generated text rather than authentic work.<\/span><\/p>\n

To use an example closer to home, if a client were to use AI detection to verify your agency\u2019s work, they could easily turn up false positives. That\u2019s why it\u2019s critically important to be aware of how accurate AI detectors are\u2014in a world that increasingly relies on this technology, it\u2019s important to educate your clients on the possibilities, limitations, and potential inaccuracies of both AI and AI detectors.<\/span><\/p>\n

How do AI checkers work?<\/span><\/h2>\n

AI checkers work\u2014interestingly enough\u2014in much the same way AI tools work. That is, they use algorithms, machine learning, and natural language processing (NLP) techniques to \u201clearn\u201d the patterns inherent to natural human text and AI-generated text. Just as AI tools like ChatGPT and Bard are trained on real-world information and human writing, AI detectors are trained on large datasets containing both types of information which help the models identify text that follows the patterns most commonly associated with human writing or AI-produced writing.<\/span><\/p>\n

In the world of generative AI, two concepts repeatedly come up as tools for gauging the quality of output: perplexity and burstiness. These concepts also underlie how AI checkers work. If you want to learn <\/span>how to avoid AI detectors<\/span>, familiarizing yourself with these is a must.<\/span><\/p>\n

Understanding perplexity<\/span><\/h3>\n

Perplexity is a measure of how accurately an AI can guess the next word that should appear in a sentence. This is developed with pattern recognition based on the analysis of large datasets, and the lower the perplexity of a model the more correct its output will be.<\/span><\/p>\n

Consider an example: Given the sentence \u201cI chewed some gum and blew a big (blank)\u201d, an AI with low perplexity would likely guess the word \u201cbubble\u201d. Most of the time, when you hear this phrase in real life, that <\/span>is<\/span><\/i> the most likely word to follow. A less likely but still correct next phrase might be \u201cwad of cash\u201d. An AI that shows mid-range perplexity might come up with that phrase based on context, but it\u2019s less likely. An AI with very high complexity might say \u201crollercoaster\u201d \u2014 that doesn\u2019t make sense, and it wouldn\u2019t be a useful, practical AI model.\u00a0<\/span><\/p>\n

As you can see from this example, NLP models exhibit a sweet spot in terms of perplexity. If they\u2019re \u201coverfitted\u201d to the data on which they were trained, they will only work for narrow use cases but won\u2019t generalize to other applications. A general AI tool like Chat GPT must exhibit enough perplexity to apply to different subjects, while an AI used internally at legal firms should more narrowly conform to its training data.\u00a0<\/span><\/p>\n

Understanding burstiness<\/span><\/h3>\n

Burstiness is a measure of how regularly words, phrases, and syntax patterns appear in a text. Natural human text is generally \u201cbursty\u201d. When we speak, we vary the length of our sentences. We might follow a long, complex, run-on sentence with a short, simple one. <\/span>
\n<\/span>
\n<\/span>Text produced with AI models tends to be more consistently rhythmic and, well, <\/span>robotic<\/span><\/i>. Certain words or themes may be repeated more than in natural human text. Let\u2019s take a look at an example.<\/span><\/p>\n

A high-burstiness model might produce the following sentence:<\/span><\/p>\n

\u201cThe unmistakable aroma of freshly baked bread wafted through the house, filling the air with warmth and anticipation. It smelled so good that I woke up from my deep sleep. I was salivating.\u201d<\/span><\/p>\n

A low-burstiness model might look more like this:<\/span><\/p>\n

\u201cI woke up to the pleasant, inviting aroma of baking bread. The baked bread was soft, warm, and comforting.\u201d<\/span><\/p>\n

The first example shows varied sentence lengths and a variety of words, while the two sentences in the second example have a similar length and structure. The phrases \u201cbaking bread\u201d and \u201cbaked bread\u201d exhibit a repetitive pattern that doesn\u2019t feel particularly natural.\u00a0<\/span><\/p>\n

These two measures\u2014perplexity and burstiness\u2014are used by AI detectors to score output. Generally, high-perplexity, high-burstiness text is more human, while low-perplexity, low-burstiness text is more likely to be AI-generated.<\/span><\/p>\n

How valid are AI detectors?<\/span><\/h2>\n

The proliferation of AI has marketers, especially those working in SEO, asking themselves how accurate AI detectors are.\u00a0<\/span><\/p>\n

Today, AI detectors face limitations in accurately discerning between AI-generated and human-produced content: while they can be useful for spotting AI-generated output due to familiar patterns from NLP models, they struggle when it comes to human-generated content, leading to false positives.<\/span><\/p>\n

It\u2019s easy to understand why this is the case. Biases and limitations of AI detectors arise from the fact that even human content, especially when written for the web, can be relatively low in perplexity and burstiness. We may speak in complex, unexpected bursts, but we tend to write in a more formulaic way.\u00a0<\/span><\/p>\n

A <\/span>2023 study<\/span><\/a> evaluating various AI content detection tools confirms this, showing higher accuracy in identifying AI content (especially when produced by GPT-3.5 rather than GPT-4), but lower accuracy for human-generated content.\u00a0<\/span><\/p>\n

Should you bypass AI detection?<\/span><\/h2>\n

Even if AI detectors aren\u2019t foolproof, if you have an <\/span>AI startup<\/span><\/a>, you should prioritize bypassing AI detection. These tools are likely to become more sophisticated, and while the role of <\/span>AI in digital marketing<\/span><\/a> will only grow, successful agencies will leverage this powerful new technology ethically and carefully.<\/span><\/p>\n

The reality is that AI is here to stay, and there\u2019s no need to constrain your business by eschewing it altogether. Different scenarios and use cases demand varying approaches to bypassing AI detection. Let\u2019s take a look at a view.<\/span><\/p>\n