The Ethics of Artificial Intelligence

As the usage of artificial intelligence has grown exponentially, concerns have been raised over the ethical usage of artifical intelligence. It has been widely accepted that bias exists in AI, and impacts outcomes. Another key factor is how AI has been used, while keeping bias and ethicality in mind.

Since the early days of development, concerns have been raised, such as when AI labeled African American men as “Primates.” One of the fundamental issues is data collection and labeling. Before training models, data is labeled, which is where some problems arise. Studies show that having a dataset that has too many of a certain type can cause issues, known as class imbalance. This has been shown to be an issue in certain cases, such as medical research and credit card fraud, where the cases that are being looked for aren’t properly represented. 

One question that is starting to be raised is how should AI help us. For some, it should be treated as a tool, while others won’t come near it. This is prevelant in schools, where teachers must decide what is a good use, in the spirit of education. Also how should it be used to replace jobs? By allowing AI to take entry level jobs, how do new hires ever reach that senior position without going through the system.

Moreover, there are ethical concerns over computer vision and its usage. One key thing to highlight is the increased usage of it, and how it has been used. For example, one company has received criticism for its surveillance of vehicles, with some going as far as to call it one of the largest surveillance operations in history. 

This raises further questions, along with concerns of misuse of technology, given that the company has remained opaque on its policies. Moreover, it has been shown that the software has been used to enforce policies that are concerning, along with the risk of a backdoor, given the amount of people who have acess to the national database.

When developing advanced technology, we must take a step back, and consider how it may be misused, and how that can be prevented, along with how we can prevent biases in AI, to prevent systemic injustice. We also must consider what we do with all of this newfound power, and how we can seize the benifits without being harmed.

Works Cited

Brewster, Thomas. “AI Startup Flock Thinks It Can Eliminate All Crime in America.” Forbes, 3 Sept. 2025,   https://www.forbes.com/sites/thomasbrewster/2025/09/03/ai-startup-flock-thinks-it-can-eliminate-all-crime-in-america/.

Whittaker, Zack. “Lawmakers Say Stolen Police Logins Are Exposing Flock Surveillance Cameras to Hackers.” TechCrunch, 3 Nov. 2025,   https://techcrunch.com/2025/11/03/lawmakers-say-stolen-police-logins-are-exposing-flock-surveillance-cameras-to-hackers/.

“Flock Roundup.” American Civil Liberties Union, 2025,   https://www.aclu.org/news/privacy-technology/flock-roundup.

“Bias in AI.” Artificial Intelligence Hub, Chapman University,   https://www.chapman.edu/ai/bias-in-ai.aspx.

Mac, Ryan. “Facebook Apologizes After A.I. Puts ‘Primates’ Label on Video of Black Men.” The New York Times, 3 Sept. 2021,   https://www.nytimes.com/2021/09/03/technology/facebook-ai-race-primates.html.

“Handling Imbalanced Data Sets.” Google Developers Machine Learning Crash Course, Google,   https://developers.google.com/machine-learning/crash-course/overfitting/imbalanced-datasets#step_2_upweight_the_downsampled_class.