can you detect ai writing

can you detect ai writing

Detecting AI Writing: An Essential Guide

1. Introduction to AI Writing

Even with an increasing breadth of consumers and a galaxy of generators, the wide distribution and consumption of AI writing do not come without challenges. There is the potential for AI to be maliciously implemented, for example, to spread propaganda or disinformation. It may also generate copyright infringements and liability issues. Lawsuits were filed by the authors of the original work claiming that their copyright was infringed by the unauthorized copying and publication of their authentic work as AI-generated text. To protect innocent end-users, it becomes increasingly important that software programs capable of performing such types of analysis are made publicly available. Such repositories can also provide reproducible, controlled analysis on different AI-generated texts from any subject. In this article, we introduce a comprehensive, cross-disciplinary, and fundamental guide for detection of AI-based Greek writing on different writing platforms. This guide may also be adjusted for AI writing in different languages, because the main writing technique is for all humans. Our work focuses exclusively on the detection of original Greek text writing. Following the same methods but changing the general techniques can achieve the so-called detection of AI writers in all languages. With the necessary modifications and extensions easily, this guide also applies to other writing technologies.

Artificial intelligence (AI) has been making substantial advancements in writing for a while now. These advancements include natural language processing, sequence and language modeling, and the creation of multimodal language in which language models also process visual information. More recently, AI has moved into the area of language generation, such as turning prompts or cues of simple instruction into coherent, engaging, and informative articles, stories, and poems. AI writing is discovered and digested by a variety of consumers. These include readers, content reviewers, advertisers, and website administrators. With the significant portion of AI-generated writings such as news or opinions, advertisements, political endorsements, e-commerce reviews, and awarded grades for non-existent books, movies, and products, the scope of AI writings keeps rapidly expanding. This evolving landscape of AI writing can also lead to potential forgeries or misuse.

2. Signs of AI Writing

If an AI has written the email, essay or report, then you’re sure to notice a lack of spelling and grammar errors. Not only that, but the overall structure will also be better than expected, with much higher writing quality than seen in what has come previously. In contrast, AI language models are more than capable of producing text that’s coherent, easy to understand, and logical. After all, that’s exactly what they have been trained to do. That’s why one of the tell-tale signs of AI writing is a lack of these markers created by it. The better the AI, the fewer the mistakes, and the more progress it has made in eliminating these markers.

But visual checks can only take us so far. To really identify AI writing and catch out a text model’s accuracy, we need to add scientific methods to our checks.

– The writing appears too perfect to have been created by a human. It looks as though paragraphs or sections of text have been copied and pasted directly to create a longer piece (“cut and paste syndrome”). – It repeats the same idea using slightly different words, or reuses the same phrase several times. – It changes subject abruptly, e.g. moving from writing about business operations to discussing a person’s career or achievements. – More generic sentences that can be relevant in many situations are seen, especially in longer writing.

There are several tell-tale signs that an email, essay or report has been written by an AI language model. Common giveaways that a machine is doing a human’s work include the following:

3. Techniques for Detecting AI Writing

If you have little knowledge of how AI writing models work, the retuning of such models is labor and resource-intensive. Manually identifying specific patterns and characteristics of writing that should mark AI with near certainty is more straightforward but still involves a lot of work. Research in these areas is currently very much in the early stages because the arms race between writing models and detection models hasn’t finished, said Amazon AI’s Professor Neil Lawrence, who added that this ongoing game of chicken means detection models haven’t been optimized for in the same way the writing models have.

Retraining models: Understandably, one approach to identifying when AI has been used to write an article or text is to pass the text through an AI model that has been trained to detect outputs from other models. A study by OpenAI showed that models trained on large, diverse datasets of text can detect the outputs of large language-generating models. However, both OpenAI, which developed and released the GPT-3 writing model, and the New York Times, a large news organization, have launched training datasets to detect such outputs, and even with the advantage of prior knowledge, OpenAI’s and the Times’s models were only right 52% and 78% of the time, respectively.

Short of a guaranteed way of detecting AI-generated writing, there are several methods available to journalists, news organizations, researchers, editors, and readers. Here are four possible approaches that have been suggested, are currently in use, or are under development.

4. Challenges in Identifying AI Writing

It’s become commonplace over the past year to describe AI-generated text as “deep fakes for text.” I’ve used that comparison myself a few times. But it deserves a few words of clarification. Deep fakes refer to convincing images and videos created by AI, in which people’s expressions, voice, face, body, and surroundings can be cloned or manipulated so that it’s hard to tell the difference between that creation and an analogous scene in the real world. Presumably, the term deep fake is borrowed from its antecedent, and sibling, from a previous era: Photoshop.

AI systems have now created an alarmingly wide range of textual content, from spam, online abuse, hate speech, fake news, reviews, to automated customer service, trolling, and more. In response, scientists are using machines to spot machine-generated text with growing speed and success. But with each leap in language generation, matching leaps can be expected in text pretending to be human. As so often, the computing world is confronted by what Alan Turing called the “Imitation Game.” Here’s a quick guide to what’s happening in an endless contest between identifying and pretending, and why it’s so complex and critical.

Many challenges are involved in identifying AI writing. Because there’s so much diversity in the text AI produces, it’s hard to predict the specific problems. But analysts should be alert to warning signs like those described below. Always keep in mind that the absence of such signs doesn’t sufficiently prove that no AI-written text is involved. Nevertheless, rapid progress in ML can be expected to exacerbate these challenges in ever more surprising ways.

5. Implications and Solutions for AI Writing Detection

The result from our AI detection model only applies to GPT-2 from OpenAI. However, we expect that discoveries and tricks used to circumvent detection will be valuable for GPT-3 and LLM models as well as those being developed in other projects. In the future, writing instruction or quantification of AI/teacher sharing can be improved by checking student work for AABB rhyming and consonance patterns, identification of obscure entities via Coreference, or common topic-specific prompts. For inflation detection, we add all Hamiltonian path constraints in the morphed query used in the response procedure and prevent the use of entity filling operations.

In this section, we consider the implications of AI writing detection and provide suggestions for those looking to circumvent detection, or in contrast, for those striving to improve writing assessment and the prompt design. Note that our results are based on a specific type of GPT-2 model. Reactions to produced texts will be different based on context and data. Inferring some general advice about detection systems can be insightful. However, the confidence of indicators such as high style similarity should decrease when applied to documents coming from models with more training data or those fine-tuned on a narrow domain and prompt-scope, for example, the more recent OpenAI models or models trained on LLM.