how to detect ai writing

how to detect ai writing

How to Detect AI Writing

1. Introduction

The reasons for creating such accounts are diverse but in most cases malicious, often aiming at online spamming, fraud, disinformation, or social engineering. Online providers often make use of automated dialogue systems (ADS) to process queries but the acceptance and use of such systems has been debated because they are sometimes designed to “be indistinguishable from that of a human beings”, which could easily raise ethical concerns. Online providers also have a need to counteract different attempts of ADS attacks. However, some recent research argue that experienced users are not easy to mislead by excluding humans or bots before they answer questions.

A recent paper presented an interesting example of AI-generated text, which raised the question whether AI-generated text can be reliably detected. We simulated a large number of sockpuppet user profiles that were suspected of being created by an automated dialogue system (ADS), and a second expert group analyzed this test case, checking whether their decisions were consistent with the computer judgement.

2. Common Characteristics of AI Writing

If the content of the offence, county of expressing emotions can also be faced charges specifically about the security concern, it “included insult, or usually the plagiarism, the way that AI writers they are performed it are noticeable. Disproportionate social moral and even threats of physical violence is commonly known texts, writers created with anti-social and ethical norms of emotional, autonomous and irreversible systems are included in the emulator that has been ignored. Powerful research has shown that the AI system cannot be aware of possible mishaps and struggle the timely implementation of a stop to stop the generation of harmful texts. Random large-scale text generation models output texts conform to the training distribution or distribution, rather than Bayesian considerations. As a result, anomalies could yet serve the output – there are no real perks regarding the probability of equivalences.

Of course, there are some features of AI-written texts that can more or less unambiguously testify that the text was written not by a human being, but by a machine. The symptoms of the coherent and logical ends of the text are not specific to the text in a particular and composers of this type of large scale, they repeated at random every language, by which various organizations generate a huge amount of content. Many times the repeated by identical three random, exclusive dictatorship, or they themselves, as in writing, writing “ready for handing in” student sizes. It should be noted that not all texts that recorded roughly the same phrase, written by AI, traditional not written by a machine, because, same, a writer can sometimes be repeated after itself casually or of e in the process of writing against being by irrelevant and senseless generation speech(s)ntetically(repeatedly the books and articles, or.

3. Techniques to Identify AI Writing

In practical terms, Three-Biases was meant as a way to encourage skepticism when presented with an argument whose only substantive claim was accuracy of prediction, especially by AI. In the case of text generation, model-based text generation could produce human-like texts, even though they lack human-like meanings. To ask of a generated text: is it argumentative, is it original, and is it convincing does not go so far as to genuinely understanding the meaning of a text, it does use close clues, summative act of constructing the text, and deference to other human work, such as similar texts. The arguments gathered against it, however, also hinge on the close connection between AI and human text. Even if human readers feel that they assign higher THEM scores (regarding argumentative, originality, and convincingness of a given text, than AI, the third property used by the authors of GPT2-117M to expose that their model, and language models more generally, do not fully understand human text, could still be used for detection (H3). In other words, if texts produced by language models could be said to have a ‘hallmark’, AI models themselves could detect it, even without any access to training data.

In other words, if AI writing could be said to have a ‘hallmark’, unlike human writing, it would not be an unusual word choice or type of argument, but instead simply accurately strung together words that are more likely to be found in published texts. This argument drove our first hypothesis (H1), which posits that the more a piece of writing appears to be generated by a language model, the more likely it is not to be a human-written text. In order to test this hypothesis, we generated three datasets of writing: the first was a dataset of human-written texts, for which we used 2001-2002 AP essays made available as a training set in a Kaggle competition. This dataset was then compiled after the discovery of misattributions in the text generation in OpenAI’s GPT2-117M in 2019. The other two datasets were then generated using OpenAI’s GPT2 model conditioned on each text. For attacking our hypothesis, three different strategies could be used, depending on access to the model that was used for the generation of the test dataset: envisage the best strategy to identify human text, and then use the strategy to identify it.

4. Tools and Resources for AI Writing Detection

The characteristics that can help to distinguish AI from human writing are myriad, and their similarities warrant machine learning approaches. For the past several years, the text, web page, and university site-sharing platforms and caches of these platforms with enhanced social media platforms that recently debuted have demonstrated the potential for producing an enormous amount of writing. Although writing AI-assisted text has dramatically improved over the years, it still has trouble generating human-like outputs. Bayesian classification has been used with word frequencies from Naïve Bayes to determine the degree of humanness of a text by Tianyi Wang. N-gram models have been adapted to determine whether those models generate too many high entropy near-minimum messages. And the best-known writing-checking services and some (a few) third-party AI-writing-checking services are a widely respected method for detecting AI writing.

The quantitative characteristics of correct text that can be used to detect AI writing typically are grouped into two categories: those that represent the underlying grammatical structure that gives a particular language its form, and those that represent statistical patterns of word use. Although they can be used individually, most composite approaches employ a pipeline utilizing one or more of each type of measure. There has been considerable research on readability formulas for written English that reveal which prose pieces do not exhibit a simple grammatical structure. Many of the differences fostered by grammar and structure are qualitative and conventional; for example, structural difference in style is what makes a bad or stilted sentence.

5. Conclusion

Lastly, with our empirical study, we highlight a variety of potential discussions and future works. We propose to uncover the characteristics of human writing and AI writing, and check if our model can be used to detect other NLP outputs. It is interesting to validate the study with various AI writing channels, especially different topics written by the same Poet AI user. If controversial characteristics between AI and human indeed contain extra information about these two types of writing, we may consider using them to further boost the both types of writing performance. The deployment, adoption, and circumvention of the detector system would bring a new wave of research in multiple fields, such as social media related fields (bias, echo chamber, content manipulation), AI characteristics, and AI psychology.

This project is the first study of its kind, which aims to identify the AI writing in large-scale social media text. We propose two pipelines to detect the AI writing using supervision. Our supervision signals are the time-sensitive and content-biased distribution patterns of the human and AI writing. We assess the AI/Non-AI modeling performance using wide-ranging classifiers. We find that the supervised methods outperform unsupervised methods, while non-sensitive methods outperform sensitive methods. We also compare the modeling performance of AI writing, Urban Dictionary text, and influential political officers’ tweets. Among the three types of writing, AI writing detection is easiest and richer models are certainly the optimal choice in terms of both precision and recall. However, in the real world, the performance gain of using richer models would need to be weighed against its cost in an actual deployment. Nevertheless, with the trend of AI usage in NLP, it is important to consider the potential this arms race between AI writer and AI detector.