AI Writing Tools: The Future of Content Creation
By AI Writing Detectors: What Are They and How Do They Work?
By Shouvik Banerjee | Updated July 30, 2024
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With AI writing tools on the rise and search engines depending on AI models to answer queries, there has been a growing demand for AI writing detectors. These tools, also known as AI text detectors, analyze texts to determine if they were written by an AI writing tool or not.
Academicians and professionals have often raised concerns about the use of AI content from tools like ChatGPT and Claude. It questions the ability of the users to use language correctly since they hide behind perfection…almost too perfect on many occasions. There is also the question of the credibility and authenticity of the user.
These AI detection tools employ multiple language models and machine learning techniques to compare the users writing with a vast database of resources, including books, articles, websites, and academic papers. Some advanced detectors can even detect paraphrased, rearranged, or translated content, thus providing a more comprehensive analysis than its basic counterparts.
In this post, I will give you a short tour of AI writing detectors, what they are, and how they work.
Why is it important to detect AI content?
The primary purpose of an AI writing detector is to differentiate between AI-generated and human-written content. They are much different from plagiarism checkers whose primary function is to identify copied content from existing sources. AI writing detectors specifically target content that is created by AI text generation tools like ChatGPT.
Here are some areas where it has become almost necessary to detect AI content:
Academics
AI writing detectors are becoming indispensable in educational settings as more schools and universities adopt AI detectors to scan essays and homework. At the University of California, for example, professors are using Turnitin’s advanced AI detection capabilities in their submission process for essays and assignments. When students submit their work, the system automatically scans the text to identify AI-generated content.
In one instance, a student submission flagged by the detector revealed extensive portions that matched typical GPT-generated patterns. Due to timely intervention, the professor could guide the student in the right direction.
The primary goal of educational institutes is to preserve academic integrity. At the same time, they want to ensure students are genuinely engaged in academic activities. Therefore, it makes sense for institutes to rely heavily on AI detection software.
Content Moderation
The rise of smartphones and cheap internet has created a wave of spam, fake reviews, and misinformation on digital platforms. This poses significant challenges to maintaining trust and credibility. To address these issues, many platforms have turned to AI detectors, which leverage sophisticated algorithms to identify and mitigate such harmful content effectively.
For example, Microsoft Outlook has a powerful AI-powered spam filter to scan incoming emails. These filters identify common spam characteristics such as unsolicited promotional content, suspicious sender information, and the use of certain keywords. Many times, emails sent to me by genuine users have also landed in the spam folder.
Journalism/News
In journalism, facts are everything. But with the rise of fake news, it has now become more important than ever to fact-check every line. Moreover, the standard of fake news has become so high that sometimes it is hard to differentiate it from real news.
In this field, AI writing detectors analyze textual patterns, linguistic characteristics, and user engagement metrics to identify and flag sensationalized or misleading content. For instance, Facebook employs machine learning models to scrutinize the headlines and body of articles shared on its platform. By focusing on clickbait signals such as exaggerated claims or emotional language, Facebook’s AI systems can demote or limit the reach of dubious content, ensuring that users are exposed to more reliable and informative posts.
Another great example is Google News employing a similar strategy. It leverages AI to prioritize content from reputable sources while demoting articles identified as clickbait. The AI algorithms analyze engagement metrics such as bounce rates and dwell time to detect articles that attract clicks but fail to deliver substantive information.
How AI Writing Detectors Operate
Now, moving on to the more technical aspects of AI writing detectors.
AI writing detectors use Machine Learning algorithms to analyze text patterns, sentence structures, and other linguistic features and compares them with a content pool.
Since there are complex technicalities involved in the functioning of an AI detector, I will cover this section in a separate blog post. For now, let’s explore some of the mechanisms powering AI writing detectors.
Language Models
AI writing tools and detectors use the same language models. Below are some of the most commonly used Natural Language Processing (NLP) algorithms by AI writing detection tools:
- Transformer-Based Models: Transformer-based models are important in AI content detection because they can help identify contextual relationships and long-range dependencies within any text. These models, like BERT and GPT, analyze patterns and anomalies that might indicate the use of AI. They may not be perfect but the more they train on large datasets, the more they learn how to differentiate between the more naturally occurring human language and the more uniform structures produced by AI.
- RNN-Based Models: RNN-based models, including architectures like LSTMs (Long Short-Term Memory) and GRUs (Gated Recurrent Unit), identify patterns in any given text that distinguish human writing from AI. The main patterns that these models study are the flow and coherence that are typical of human writing. For example, while studying fake reviews, RNN-based models identify recurring patterns which it has learnt from large amounts of datasets. AI-written content often follows similar patterns while human writing is unique to every individual.
- Hybrid Models: Hybrid models combine the powers of both Transformer-based and RNN-based models to provide the best detection results.
Examples of Popular AI Writing Detectors
While there is a plethora of tools available, there are only a handful that provide accurate results. Here are some of them:
Originality.ai
About: Originality.ai is one of the best AI detection software that is designed to ensure the authenticity and originality of digital content. Through its advanced machine learning algorithms, the platform accurately offers real-time analysis and immediate feedback. Besides detecting AI-generated text it can also detect plagiarism, making it an invaluable tool for writers, editors, and especially educators who require unique and original text.
Features: Originality.ai has a user-friendly interface with features like detailed reports and customizable settings, allowing users to efficiently pinpoint and rectify issues. It supports multi-language text analysis and offers API integration for seamless integration with larger apps.
Pricing: You get some free credits if you use their plugin. Otherwise, you have to pay anywhere between $30(for the pay-as-you-go-plan)-$14.95/month to access the full range of features.
GPTZero
About: Although GPTZero specifically targets content created by models like GPT-3, it can also detect text created by Claude, Gemini, or LLaMa. Therefore, this is a useful tool if you rely a lot on ChatGPT or other similar AI tools. GPTZero analyzes textual patterns, structure, and other linguistic features to determine if the piece of text was generated by a GPT-based algorithm.
Features: GPTZero does deep scans, provides detailed reports, has a Chrome extension for quick checks, and supports integration with other systems via API.
Pricing: It can cost you anywhere between $10-$23/month to scan anywhere between 100,000-500,000 words.
Scribbr’s AI Detector
About: Scribbr’s AI Detector is yet another AI detection tool that is making waves in the AI market. Just like the other two, this tool also uses advanced machine learning algorithms to detect various linguistic elements and patterns inside the text.
Features: It’s a user-friendly platform that generates comprehensive reports that not only identifies AI content but also provides explanations. Moreover, the tool seamlessly integrates with Scribbr’s suite of plagiarism detection services.
Pricing: Scribbr has a forever free plan with a 500-word limit. You can also pay $180 annually or go for the enterprise plan for more features.
Reliability and Limitations of AI Writing Detectors
AI writing detectors are great but like all other software, especially AI, they have flaws. While these tools are constantly improving, it is important to know what you are getting into when you start using them.
Some of the common problems are:
1. False Positives and False Negatives
This one is a huge disappointment. Most people have complained that AI detection tools lack accuracy and it is the leading reason why AI detection tools are not entirely trustworthy.
False positives occur when legitimate human-written content is incorrectly flagged as AI-generated. This can frustrate users. For example, a student’s original essay might be incorrectly flagged, complicating the grading process and unfairly questioning academic integrity.
False negatives occur when AI-generated content slips through undetected. Despite the advancements in machine learning, AI models can become sophisticated enough to mimic human writing closely, making it hard to detect AI content.
2. Dynamic Nature of AI Models
AI writing models like GPT-3 are continuously evolving, which presents a moving target for detection systems. This requires constant adaptation.
3. Contextual Understanding
AI detectors can struggle with contextual understanding, making it difficult to discern subtle nuances. For example, in literature reviews or creative writing, AI detectors may misinterpret stylistic choices or fail to recognize genuine human creativity, leading to false flags.
Final Takeaway
As AI writing detectors continue to evolve, their role in ensuring content integrity and authenticity will become increasingly critical. Innovations and research in machine learning, improved contextual understanding, and ethical considerations will hopefully improve accuracy and reliability.
(Note: Part of this blog post has been structured and written with the help of Microsoft Co-pilot, Babbily, and Scalenut. However, it has been edited and proofread by a human writer.)
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Shouvik has been writing for the last 8 odd years and has covered almost every field including copywriting, social media posts, and ghostwriting for blogs. He even has a published book to his name and a couple of short fiction in literary magazines.
