free ai writing detector

free ai writing detector

The Importance of AI Writing Detectors

1. Introduction

Plagiarism and ethics checking software are examples of tools available to aid the detection of duplicate or ghostwriting cases. Widely used academic conferences and journals are also creating deterrents to help identify unsavory patterns of behavior. In the ICLR conference, for example, authors are required to explain explicitly the circumstances in which they used another person for the review process (e.g., Did the person have any influence on the final written results?). In CMT, GPA, and other conference management systems, efforts in deploying language models to help in identifying ghostwriting become a reality. Governments and funding agencies also keep a close eye, increasingly asking for information on the identity of the writing author. Recent guidelines from the National Science Foundation (NSF), for example, appear in funding solicitations, explaining how to deal with the presence of ghostwriters in the report of scientific articles.

Scientific writing is a specific type of writing in academic environments. One prominent goal of such types of environments is to clarify research ideas through publication. Consequently, detecting if any portion of the text being reviewed was influenced by a third party becomes extremely important. The use of external language to help the writing of scientific articles may be seen as rigging the peer review system, leading to academic fraud, financial repercussions, and ethical issues. Therefore, it is the responsibility of conference organizers, journal editors, and reviewers, as well as funding agencies and institutions, to work together to create deterrents against such acts. For this, various innovations in review processes and available tools may help to keep a professional and honest scientific community.

2. Understanding AI Writing Detectors

The goal of this article is to concisely (and almost technically) summarize my 25-page paper that I presented at the 5th International Workshop on Detection, Representation, and Exploitation of Events in Society and Online Media. I focus on the importance of writing detectors because not only did this process create gaps inside my research that ended up becoming even stronger, more tested ones when compared to the detectors available or listed in the literature, but also because during the Rewards for the Rescues Feature of my Capstone, the writing detector to measure attribution, detection, or counterfeiting was part of the missing piece faced by professionals other than me. Even my educational games, from sidebar 3, require the AI writing detector methodology to function properly.

Imagine that you are a university professor and that you have hundreds of students taking your course. You assign an essay and days later, as you start reading them, you notice that two essays are identical – except for the names of the students. Or suppose that you are a publisher who is sent daily hundreds of submissions for new Christmas stories. You accidentally discover that dozens of these short stories are plagiarized, but copied in such a way that the plagiarism detectors do not detect them. Unfortunately, I faced both of these situations in real life. In fact, with the latter story in particular, not only did it happen to me – but also to numerous other individuals working at the same publishing company. The saddest part is that all of the submitted plagiarized stories were so similar to those already published that with my five years of rigorous training, I was only able to pinpoint 29 – out of more than 80.

3. Benefits of Using AI Writing Detectors

The second advantage is what is known as their reliability, or consistency. Where teachers may disagree in their scoring of an essay at rates in the 25-40% range, automated systems have been shown to disagree far less and that systems can provide more consistent scoring than human raters. Computer-scoring programs use a consistent, repeatable system for assessing students’ texts, and they ignore potentially prejudicial student attributes such as gender, race, or ethnicity. In effect, all students receive a fair and equitable evaluation of their texts. Nor do these systems grow tired, bored, or fatigued, and because of this, the scoring of an interminable number of essays does not fatigue or degrade the quality of a student’s texts as it might the human grader. Other investigators have also sought to determine if automated evaluation possesses a greater “hybrid” value when it pairs the strengths of automated evaluation with those of human perception. For example, Dikli et al. used a “high-stakes immigration exam that contains multiple-choice and integrated tasks for approximately 40,000 applicants.”

The first benefit of automated writing evaluations is their speed. Within seconds, a student can submit an essay and instantly receive feedback on several traditional writing skills, such as grammar, spelling, and punctuation. This nearly instant feedback is what Schwartz notes to be “the greatest promise” of computerized composition fields today, and it clearly outlines why these programs are being used in thousands of schools, colleges, and universities across the world. Texts can be scored at any time of day and across multiple time zones, providing the same feedback to all students, regardless of their working hours or class time slots.

4. Limitations and Challenges

GADGET uses only a single source. While it would be statistically nonsensical for a 90% automatically generated site to be manually written by 9 malicious individuals at some point in the future, it certainly would not be impossible for several different writers to independently author pieces of content, all contributing to an AI-generated page (the kind of activity Sprayberry is designed to reveal). The prospect of adversarial examples discussed in §4 makes the challenge of detecting this form of content significantly lower, such that a model attempting to classify pages based on shared attributes could still scarcely distinguish these instances from common, non-AI generated webpages. Additionally, while the model that trained it would undoubtedly be improved with a diverse sample of training data, GADGET’s use case requires it to handle completely unfamiliar-sourced content equally well, regardless of its training. This property is somewhat similar to justifying a domain-agnostic deployment, such that there are relatively few challenges to encountering completely unexpected content models have not seen before.

Research on the detection of automatically generated content is in its early stages; however, many challenges have been identified. Posing AI indicators with the task of identifying automatically generated text often leads to the situation where these indicators instead learn the tell-tale characteristics of the offending author. Also, while indicators commonly view automatically generated text as a simple extension of instances where authors are attempting to hide an unwanted influence, they do not necessarily examine actions taken to obscure the system’s origin. The ability of adversaries to fool indicators presumes the system can plausibly learn to distinguish between these two phenomena, achievable by capturing strange inconsistencies in style or behavior of the submitted content. More specialized forms of Robust Physical Perturbations (a specific subset of Adversarial Examples) exhibit this characteristic when applied to CAPTCHAs and other vision-based machine learning models, though generic text generation – which in turn can produce strange inconsistencies for any attribute distribution – does not necessarily break a similarly well-trained model.

5. Conclusion

But large collections of expert-scored essays are not free, and securing funding for the types of tasks that fully support the creation of such corpora is often difficult. Private funding sources, especially in the case of industry-backed investigations, expect some return from AE research that goes beyond the field of high-stakes educational measurement. Also, while there is literature examining the relative effectiveness of various AE systems (including ensemble systems), these studies often do not publish enough specifics about the quality of the test essays or the extreme ends of the expertise spectrum to make meaningful comparisons. These data often reside in the hands of a business entity that treats the scoring method as proprietary information, and when they do share results, it may be with those who have signed agreements not to disclose specific details about the evaluation. Alternatively, a party interested in the quality of a specific AE tool might obtain a license to utilize it in their specific setting, and the licensing company would supply some results as part of competitive agreements.

The effectiveness of automated essay scoring systems, or writing detectors, drives much of their importance. The most widely used automated essay scoring systems fall under the category of supervised machine learning systems, and in these systems, the quality of the automated essay scores relies first and foremost on the quality of the expert-scored training essays that are the foundation of the scoring models. This emphasis on supervised learning is not unique to AE systems, as few supervised machine learning models can outperform the quality of their training data, but it is of particular consequence for AE systems. AE developers and researchers are aware that the open-ended nature of writing means that written products, as naturally constructed and flexible as those in a human essay, accommodate a wide variety of interpretations, opinions, and qualities. Therefore, the selected student essays used to build AE systems need to also represent a broad range of interpretation, opinion, and quality.