Whenever AI causes harm, or simply just controversy, someone inevitably tries to cushion the blow by pointing out that it’s just a tool. The implication is that the technology itself is neutral, like a hammer or a calculator, and that any moral questions belong entirely to the user.

That idea is emotionally appealing as it relieves responsibility and suggests we can keep building and deploying powerful systems without having to argue about ethics, politics, or human consequences. But it’s increasingly false.

AI isn’t neutral in the way people mean it. Not because AI has opinions, but because AI is built out of choices. Choices about what data counts as knowledge, what outcomes count as success, what risks count as acceptable, what behaviors count as safe, and whose experiences count as normal. Those choices embed values in the system long before a user ever touches it.

The “neutral tool” myth breaks down for a simple reason: AI is not one tool, but rather it’s a pipeline of decisions. And every stage of that pipeline encodes priorities.

What People Mean By Neutral and Why it Doesn’t Fit AI

When someone says a hammer is neutral, they mean the hammer doesn’t decide what to build, the user does. AI is different because it doesn’t just execute the users intent, it often interprets your request, makes recommendations, and generates content that can influence beliefs, mood, and behavior. It can gatekeep opportunities (hiring algorithms), shape public perception (recommendation feeds), and decide what information is visible or invisible (ranking systems). It becomes a mediator between people and reality.

A better analogy than “tool” is institution: AI systems behave more like institutions because they enact rules at scale, sometimes without transparency and without appeal. Institutions are never neutral. They are governed by values.

Where the Values Enter: the AI “Value Stack”

To see why neutrality is a myth, it helps to map where values get embedded. Think of AI as a stack of layers, with each layer including decisions that look technical, but are also moral.

Data is a Value Choice in Disguise

AI systems learn patterns from data. That sounds neutral until you ask: which data? from whom? from what time period?with what biases? If trained on the open internet, it is exposed to both humanity’s brilliance and humanity’s garbage, except it cannot tell which is what.  If the internet is filtered, a value judgment about what is acceptable to include is being made. If certain sources (mainstream media, academic databases, social platforms) are emphasized, certain institutions and perspectives become more prominent. If engagement-optimized content is included, the model learns that attention-grabbing language is normal.

Even the act of labeling data, deciding what is “hate,” “harassment,” “misinformation,” “quality,”........

© The Times of Israel (Blogs)