Is Generative AI Plagiarism a Growing Concern

Generative AI plagiarism is a growing concern, and it's not just about AI-generated content being copied and pasted. In fact, a recent study found that 70% of AI-generated content is not original, but rather a rehashing of existing information.

The rise of generative AI has made it easier for people to produce content quickly, but it's also created a culture of laziness where people are more focused on quantity over quality. This has led to a surge in AI-generated content that is often shallow and lacks depth.

AI-generated content is often created using pre-existing information, which can make it difficult to distinguish from original content. A study found that 60% of AI-generated content contains at least one instance of plagiarism.

Discover more: Generative Ai Content Creation

The US Copyright Office has launched an initiative to examine the copyright law and policy issues raised by AI technology, including the scope of copyright in works generated using AI tools and the use of copyrighted materials in AI training.

Major AI companies like Meta, Google, Microsoft, and Apple have argued that they should not need licenses or pay royalties to train AI models on copyrighted data, citing potential chaos and little benefit to copyright holders.

The US Copyright Office has denied copyright to most aspects of an AI-human graphic novel, deeming the AI art non-human and excluding AI systems from ‘authorship'.

Lawsuits alleging generative AI infringement, such as Getty v. Stability AI and artists v. Midjourney/Stability AI, have raised questions about the applicability of infringement claims without AI ‘authors'.

Some researchers have successfully generated nearly identical images to copyrighted films, TV shows, and video game screenshots using Midjourney's V6 model, highlighting the need for vigilance and safeguards when deploying generative models commercially.

Here are some key arguments from major AI companies in response to potential new US copyright rules around AI:

The lack of transparency and warning about potential infringement in generative AI systems can put users and non-consenting content providers at risk, potentially attracting attention from consumer protection agencies.

Plagiarism detection and mitigation are crucial in the realm of generative AI. Researchers have started exploring AI systems to automatically detect text and images generated by models versus created by humans, such as GenFace and Anthropic's internal plagiarism detection capabilities.

However, these tools have limitations, and manual review remains essential to screen potentially plagiarized or infringing AI outputs before public use. The massive training data of models like GPT-3 makes pinpointing original sources of plagiarized text difficult, if not impossible.

AI developers can adopt several best practices to minimize plagiarism risks. They should carefully vet training data sources to exclude copyrighted or licensed material without proper permissions and develop rigorous data documentation and provenance tracking procedures.

AI users can also take steps to mitigate plagiarism. They should thoroughly screen outputs for any potentially plagiarized or unattributed passages before deploying at scale and avoid treating AI as fully autonomous creative systems.

For your interest: Anomaly Detection Using Generative Ai

Here are some key best practices for AI users:

Stricter training data regulations may also be warranted to prevent the nonconsensual use of copyrighted human work to train machines. This could involve requiring opt-in consent from creators before their work is added to datasets.

Copyright and trademark issues are a significant concern in the world of generative AI. The New York Times, for instance, successfully elicited plagiaristic responses from a system simply by giving it the first few words of an actual article.

This raises the possibility that an end user might inadvertently produce infringing materials. In fact, a similar phenomenon has been observed in the visual domain, where Midjourney generated recognizable images of Star Wars characters even though the prompts did not name the movies.

Companies like Disney, Marvel, DC, and Nintendo may follow the lead of The New York Times and sue over copyright and trademark infringement. A Reddit user even shared an example of tricking ChatGPT into producing an image of Brad Pitt.

Discover more: Google Announces New Generative Ai Search Capabilities for Doctors

To mitigate these issues, filtering out queries that might violate copyright is a possible solution. This can be done by implementing guardrails in text-generating systems, but it's a challenging task that requires careful consideration of what constitutes a problematic query.

Here are some examples of problematic queries that may lead to copyright and trademark issues:

These are just a few examples of the many possible ways that copyright and trademark issues can arise in the context of generative AI.

Visual models can produce near replicas of trademarked characters with indirect prompts, as seen in examples where Midjourney generated recognizable images of Star Wars characters without directly referencing the movies.

The New York Times complaint highlighted the issue of plagiaristic responses being elicited without invoking the source material directly, and similar examples have been found in the visual domain.

A Reddit user shared an example of tricking ChatGPT into producing an image of Brad Pitt, showing how easily visual models can be misled into creating infringing artwork.

Take a look at this: Applications of Generative Ai

Midjourney generated these recognizable images of movie and video-game characters even though the movies and games were not named, raising concerns about the potential for copyright and trademark infringement.

Companies like Disney, Marvel, DC, and Nintendo may follow the lead of The New York Times and sue over copyright and trademark infringement, which could result in them winning their cases.

The difficulty in implementing effective guardrails to prevent problematic queries is evident, as seen in the case of Bing refusing to generate an image of a toilet in a desolate landscape due to "unsafe image content detected" flag.

AI companies are pushing back against potential new copyright rules, arguing that they shouldn't need licenses or pay royalties to train AI models on copyrighted data.

Major AI firms like Meta, Google, Microsoft, and Apple are making their voices heard in the debate, with Meta arguing that imposing licensing now would cause chaos and provide little benefit to copyright holders.

Expand your knowledge: Meta Generative Ai

Google claims that AI training is analogous to non-infringing acts like reading a book, and Microsoft warns that changing copyright law could disadvantage small AI developers.

Apple wants to copyright AI-generated code controlled by human developers, but most companies are opposing new licensing mandates and downplaying concerns about AI systems reproducing protected works without attribution.

Recent AI copyright lawsuits and debates are making it clear that the issue is contentious, but a multi-pronged approach is required to address plagiarism risks and ensure responsible generative AI innovation.

Here are some key arguments from major AI companies in response to potential new US copyright rules around AI:

A stronger focus on plagiarism detection technologies, internal governance by developers, and user awareness of risks and adherence to ethical AI principles is necessary to ensure that AI-assisted creation can flourish ethically.

To avoid generative AI plagiarism, it's essential to use unique prompts and parameters that steer the model away from copying existing content. This can be achieved by using specific keywords and phrases that are relevant to your topic.

Be mindful of the model's training data, as it may contain copyrighted or previously published material. According to the article, a significant portion of the training data used in popular generative AI models is sourced from the internet, which can include copyrighted content.

To ensure originality, consider using human evaluation and review processes to verify the output of generative AI models. This can help identify and correct any instances of plagiarism or copyright infringement.

For your interest: How to Learn Generative Ai

Citing AI tools can be a bit tricky, but don't worry, I've got you covered. According to the article, before using and citing AI in your work, you should consider four key factors: getting permission to use AI in your course, checking for bias in AI responses, fact-checking the content provided by AI, and citing or acknowledging that AI was used in your work.

Some citation styles, like APA Style, consider using content from Generative AI as "sharing an algorithm's output", and therefore, the author of the content would be considered the Generative AI tool. For example, you can cite OpenAI's ChatGPT as follows: (OpenAI, 2024).

For more insights, see: Generative Ai Content

In general, it's a good idea to cite or acknowledge that AI was used in your work, as this can help maintain the integrity and accuracy of your research. According to the article, the citation guidance for AI tools may change as they evolve, so be sure to check for updates.

Here are some examples of how to cite Generative AI content in different citation styles:

Currently, no citation style includes a specific reference type for GenAI content, but you can refer to the guidelines provided by official sites of different citation styles and the Referencing Guides developed by ELC for citation.

Training your own AI tools is a great way to avoid plagiarism issues. You can use your brand style guide to train AI-powered tools like Grammarly Custom Style Guides.

By taking control of what information your AI application uses, you can streamline your content process. This approach limits the scope of applications to accessing just your own work.

Take a look at this: What Are the Generative Ai Tools

You can even create a custom GPT based on your style guidelines, as shown in a step-by-step guide available online. This will help you maintain consistency in your content.

Using your own work to train AI tools helps you avoid plagiarizing someone else's content. It's a simple yet effective way to maintain originality in your writing.

As content creators, we need to hold ourselves and our community to a high standard for content quality. This includes being transparent about our use of AI-generated content.

To address plagiarism risks, a multi-pronged approach is required. This involves policy reforms around training data transparency, licensing, and creator consent. Stronger plagiarism detection technologies and internal governance by developers are also necessary. Additionally, greater user awareness of risks and adherence to ethical AI principles is crucial.

Clear legal precedents and case law around AI copyright issues are also essential. This will help ensure that AI-assisted creation can flourish ethically. Unfortunately, unchecked plagiarism risks could significantly undermine public trust.

Here's an interesting read: Generative Ai Risks

To disclose our use of AI-generated content, we should clearly state that fact along with any steps we took to fact-check or verify the information. This is especially important for topics that can impact readers' finances, such as personal finance expertise.

Here are some key considerations for responsible AI innovation:

Ultimately, if we post an article that claims to be written by a human but was generated by an AI tool, that's a shaky foundation for building trust in important audience relationships.