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Generative AI–Ethical or Not?

In such a modern world, it’s easy to get wrapped up in the wonders of technology. It enables communication, education, entertainment, and creation, like generative AI, a new tool that has taken the world by storm. It’s easy to use, accessible, convenient, and fun to interact with. The hard truth, though, is that generative AI is a thief. Trained on copyrighted material, the information spit out by these models is merely a Frankenstein interpretation of already-created, supposedly protected human work, none of which is credited or properly acknowledged. This issue is increasingly prevalent in education and media, especially in art. AI is not going to go away, but with the right measures in place, generative AI can take steps toward a more ethical system of operation. 

How does AI work, though? Defining the process used to train these models is just the first step on the journey to ethicize generative AI. Internationally published journalist and AI researcher Sarah Samuel describes the training process and theft-like nature of image-generation AI models, as well as the copyright and ethical issues within. She says, “Engineers develop these software to imitate existing artwork that selects and scrapes large datasets of images, codes, text and music from the internet. The next stage involves feeding the software this 'training data' through neural networks. During this stage, the algorithms identify and extract specific features, including shapes and colours. Finally, once trained, the generators are ready to imitate art, sometimes even in the style of specific artists. In short, engineers and software developers train these generators on creatives' artwork found online, often without their consent or knowledge” (Samuel n.pag.). AI is not only trained on copyrighted work but is also trained to imitate certain artists or writers, without their consent. This is problematic because of the AI controversy in the creative world; angry fans will push artists out of their careers under allegations of AI usage when, in reality, their work is being stolen by software that spits out a mangled recreation of hundreds of pieces. This AI “slop” can be mistaken for actual work by the artist, who gets put under the heat for the theft of their own work. Generative AI is dangerous and unethical because it steals the work of professionals, all while putting them out of a job.  

With these issues in mind, it’s also important to acknowledge that AI is not going to magically disappear. The issues involved in generative AI must be acknowledged if they’re going to be resolved. These issues are not only limited to copyright infringement, plagiarism, and creative theft, but they also include the generation and publication of false information. Technology expert and 16-time published author, Peter Wayner, explains the increasing dangers of generative AI and the steps people may need to take if it gets out of hand. In regards to the relationship between AI and false information, he writes, “The trickiest problem for users of generative AI is knowing when the AI is wrong. Machines can't lie the way that humans can, but that makes them even more dangerous. They can produce paragraphs of perfectly accurate data, then veer off into speculation, or even outright slander, without anyone knowing it's happened. [People] tend to know when they are fudging, and most have a tell that exposes their calumny; AIs don't” (Wayner n.pag.). This is yet another reason why AI is so dangerous. Its creative nature causes it to veer off into complete fantasy—or “outright slander”—when it cannot recognize a pattern or find the information it needs to generate what the user wants. In trivial contexts, this may seem insignificant, but when you put it on a corporate scale, AI tools, such as customer service bots, can cause lawsuits. Corroborating this point, author Tracy Barbour describes the current issues of generative AI, such as its biases and tendencies to come up with incorrect information, as well as how to navigate these issues in an AI-filled reality. She expresses that “concerns about GenAI [generative AI] should always start with accuracy, as AI can sometimes blend fact with fiction. ‘Hallucinations,’ as these spurious results are called, can create real problems. ‘AI can make up medical treatments that are not the best path forward, or as it did recently, recommend a food bank as a hot place to eat for visitors coming to your city.’” (Barbour n.pag.). What makes this even more problematic is that most AI users do not fact-check the information they receive from these models. This is progressively obvious in academic areas as students take the “easy way out” and dodge actual assignments, using AI to summarize information or even do their work for them. The problem here is not only plagiarism but also the risk that this information is blatantly wrong, and users don’t even think to fact-check their AI models. 

The final step in making generative AI more ethical is fixing the issues within. There are ways to make the process more accurate, lawful, and honest. People could be hired to fact-check AI and make sure all its information is up to date and true. Rules could be put in place for the usage of AI and how it operates in order to keep all parties involved safe and informed at all times. Award-winning author and CEO of AI analysis firm RPA2AI, Kashyap Kompella, describes how AI is increasingly prevalent in our society and is threatening many careers; he briefly touches on some questions concerning AI and their potential solutions. He wonders, “Is the consent of the artists required? Do the content owners and artists have to be compensated when their works are used for training? Is it practically feasible to seek consent from and compensate millions of creators?” (Kompella n.pag.). Like he says, it’s hardly obtainable to gather consent from the hundreds of thousands of creators out there whose work might be scraped from the web. However, it may be possible to rewire AI more ethically, such as hiring artists and writers to train these models rather than stealing copyrighted work off the Internet. 

Generative AI is not going to disappear. Nonetheless, if steps are taken to make AI safer and more ethical, it would improve lives rather than threatening to steal jobs. Artists’ work would not be getting stolen, remixed, and even sold by people who didn’t have any part in its original creation. People wouldn’t be afraid to make their work accessible, and a career in the arts would be far more attainable. Additionally, it would be easier to avoid false information and the dangers attached, and AI could even provide jobs to those interested in its development; it will only get better, however, if people are willing to make a change. 

Works Cited

Barbour, Tracy. "Machine Philosophy: Ethical and legal implications of generative artificial intelligence." Alaska Business Monthly, vol. 40, no. 5, May 2024, pp. 92+. Gale In Context: High School, link.gale.com/apps/doc/A793677046/GPS?u=omah21245&sid=bookmark-GPS&xid=03dbc095. Accessed 24 Apr. 2025.

Kompella, Kashyap. "Artificial Intelligence Versus Artists?" Information Today, vol. 40, no. 3, Apr. 2023, p. 40. Gale In Context: High School, link.gale.com/apps/doc/A744170275/GPS?u=omah21245&sid=bookmark-GPS&xid=765ab741. Accessed 17 Apr. 2025.

Samuel, Sarah. "Drawing a line: AI art can be fun to generate, but that doesn't mean it's ethical." This Magazine, vol. 57, no. 1, July-Aug. 2023, pp. 12+. Gale In Context: High School, link.gale.com/apps/doc/A766108648/GPS?u=omah21245&sid=bookmark-GPS&xid=f0c46d4d. Accessed 17 Apr. 2025.

Wayner, Peter. "10 reasons to worry about generative AI." InfoWorld.com, 13 Feb. 2023. Gale In Context: Global Issues, link.gale.com/apps/doc/A736896074/GPS?u=omah21245&sid=bookmark-GPS&xid=e16223cd. Accessed 17 Apr. 2025.

The morality of AI

his arguments were really good (wow, shocking)

but they were focused entirely on AI in art.

I think you can guess his position