AI feels as though it is automatic. It finishes our sentences, curates our feeds, and corrects our writing. Because AI completes all the work for us behind the scenes, we tend to treat it as neutral; something that simply exists in the background. But AI is far from independent. It doesn’t decide what matters; people do.
That assumption that AI is just “there” is what makes it easy to stop paying attention to it. When something works smoothly, we do not question it. We accept the output and move on. But convenience should never be confused with objectivity. The fact that AI feels automatic implies that it is far from neutral, but rather that the decisions behind it are easy to overlook.
Every system is trained on curated data that someone chose, organized, and labeled. Those choices define what AI models or chatbots consider “normal” and what they ignore. If certain voices or experiences are missing from the data, the system does not compensate. Instead, it reflects the absence of the information.
This issue becomes clear in facial recognition technology, as researcher Deborah Raji exposed. Widely used systems performed significantly worse on darker-skinned faces, especially those of women. Raji, a fourth-year student at the University of Toronto’s Faculty of Applied Science & Engineering, and researchers at the Massachusetts Institute of Technology are underscoring the racial and gender biases in facial-recognition services, exploring how these biases are not simply theoretical issues, but genuine failures in already deployed tools. The bias did not come from malice, but from assumptions about whose data mattered in the first place.
What makes this example important is not just the basis itself, but where the tech was being used. Facial recognition systems were already part of security and surveillance practices, meaning these inaccuracies affected real people, not just test subjects. Raji’s work makes it clear that even unintentional oversights can carry serious consequences when technology is trusted too easily.
AI affects us in everyday spaces like school. As writing and reading tools promise cleaner, faster work, they blur the line between assistance and outright cheating. This blurred line between complete assistance and aid is another reason why cheating AI use is so common within high school classes. According to research from Stanford University, about 60 to 70 percent of high school students surveyed engaged in dishonest behavior. As a writer, I have felt how easy it is to trade imperfection for polish through the usage of AI chatbots, and how this can dilute your true voice.
This doesn’t necessarily mean students are lazy or careless. It means the system rewards finished products more than processes. AI makes it simple to sound more confident than you actually are. The danger is not just cheating, but losing the space to make mistakes; to write badly before writing well. However, beyond academics, recommendation algorithms shape what we watch, read, and even believe. This is what it means to say that AI is far from neutral.
When algorithms decide what appears on our screens, they influence what feels relevant. Over time, repetition turns into familiarity, and familiarity can start to feel like truth or significance. These systems do not necessarily force beliefs on us, but they divert our attention, which is often just as powerful.
In politics, it can influence surveillance, information, and power while appearing objective. The true danger of AI is not simply that it is influential, but that it is not transparent enough. So, the question I want you to ask yourself is, at what point does convenience stop helping us think and start thinking for us?
