Generative AI Use and Controversies

In recent weeks, I shared a brief overview of the history of artificial intelligence, intending to highlight what AI is, how it got started, and how it has shown up in our lives over the last few decades. The biggest takeaway was that different forms of AI are all around us, from recommendations on our streaming platforms to predictive models that banks use to determine how likely you are to default on a loan. This technology is not new, but there is a recently developed branch of AI that is exploding in popularity and increasingly controversial: generative AI.  

Whereas AI has historically been a system that learned and analyzed existing data to find patterns or make predictions, generative AI works to create new data. Because of both the immense potential from the ability to create new works, as well as the logistical or ethical concerns that come with it, controversies and polarizing opinions of AI have arisen in academic, political, and labor circles over the last few years. That’s why today, I want to share a bit about how generative AI is being used, dive into what the common concerns or controversies are, and share resources to learn more.  

In thinking about how genAI is most often used, some of the most common examples and benefits shared are increased accessibility and capacity for creativity. An example that every person reading this has likely seen (even if they didn’t realize it) is a business that created a flyer for an event, a social media post, or a new restaurant menu using generative AI tools like ChatGPT. These businesses historically might have had to hire a graphic designer, rely on staff’s personal knowledge of Photoshop skills, or do without. Tools such as ChatGPT are saving them money, allowing them to create materials without the hurdle of learning new digital editing tools, and providing creative visuals for their business they might not have had the artistic knowledge or skill to create on their own. In this way, generative AI is often said to “level the playing field” for small businesses that wouldn’t usually have access to expensive resources. 

Along the same lines of increased access, people also are using genAI to make their lives easier and save time. This could mean having AI summarize a lengthy email and suggest a response, or a student providing AI with an essay prompt and having it edit a first draft. This could also mean using AI to set up a complicated excel spreadsheet in seconds, or recording and taking notes for medical professionals during patient appointments. Generative AI is even being used in the pharmaceutical industry to discover new drug treatments faster than human scientists could. In each of these examples (and many more, which you can read about here), generative AI is creating a tool, product, or result that saves time for the user, who can then use their time to focus on other important priorities.  

But if AI was simply making our lives better and easier all the time, there wouldn’t be so many controversies. So, what exactly are the concerns about AI? 

Some of the common concerns stem from the accuracy and reliability of the information itself. Called “AI Hallucinations,” AI tools at times can generate fictious and even dangerous information, passing it off as true. As IBM explains, hallucinations sometimes happen because such tools generate entirely new and false information, like in 2023 when a lawyer submitted court filings that included AI generated case citations and quotes that never existed. Alternatively, it can also happen when pulling from satirical or unverified sources, like how in 2024, Google’s AI overview suggested adding non-toxic glue to a pizza recipe to improve the texture after pulling the information from a satirical Reddit post. Whether it is creating fabricated information or pulling from unreliable sources, these hallucinations, when not fact-checked, can lead to real harm.  

Other controversies stem from how AI can be used or exploited for unethical purposes. One example of this concerning use is political interference, such as the AI-generated voice of Joe Biden encouraging voters to stay home on election day in New Hampshire, as PBS reported in 2024. Less intentionally malicious, studies show that biased data used to train AI can lead to unethical outcomes such as discrimination in hiring and judge sentencing, when AI is consulted. And still other ethics concerns can come in the form of data security exploits, such as a 2025 bug in Microsoft Copilot that would have allowed a malicious email to retrieve account data without the user having to interact with the email at all. When someone uses AI in bad faith, it can be a tool for both illegal and unethical activity.  

Finally, there are concerns focused on the societal impacts of generative AI’s popularity and overuse. Some express concerns about recent studies suggesting those who heavily use genAI show diminished ability to use critical thinking skills or recall information. Others focus on the environmental impacts, citing research that shows AI has led to a surge in energy and water consumption, resource depletion, light and noise pollution, and other environmentally detrimental impacts.  

Altogether, it would be a disservice to simply say that generative AI is all good or bad. The same tools that can help a struggling business with marketing or generate a video of your cat playing an instrument can also be used to mislead the public, steal your information, or contribute to climate change. The complex nature of this issue means that there are many different perspectives on the uses and risks of AI, and it’s our jobs to research and make our own decisions on if using generative AI is right for us. If you’d like to learn more about the strengths and pitfalls of AI, consider learning more through the database Opposing Viewpoints in Context, or checking out one of the books on this list.