AI Is Objective! And Other Lies We Tell Ourselves.
From Left Leaning Chatbots to racially biased tools, the data on AI is impossible to ignore. Let’s break it down.
We like to believe that artificial intelligence is neutral. No emotions. No opinions. Just facts. That belief is what makes AI feel powerful. It feels clean. It feels rational. It feels objective. But the truth about artificial intelligence is far more complex, and far less comfortable. Researchers argue that AI systems may actually act as moral covers allowing its users to perpetuate inequality while maintaining that belief that it is objective in nature.
The “Yes-Man” effect further affirms user assumptions meaning that it may at times affirm a belief which is incorrect or biased. AI often acts as a compliance engine that validates rather than challenges the thought or the behaviour of a user
We assume that because AI does not feel, it cannot be biased. But artificial intelligence does not need emotions to carry bias. It only needs data.
And data is never neutral. At its core, machine learning is simple. AI learns from data. It identifies patterns in data. It predicts outcomes based on data. This is why AI bias is not accidental. It is structural. If the training data reflects social, cultural, or historical bias, the AI will reflect the same. This is what we call machine learning bias. It is not a glitch. It is not an error. It is a direct outcome of how artificial intelligence systems are built.
A comprehensive 2024 Study analyzed that 6 leading language models showed some level of gene]der bias, measuring word frequency and sentiment to understand how often female specific language was used compared to male specific language. Chat GPT used 24.5% less female related words when compared to human writers. A UNESCO study of major LLMs found that women were described in domestic roles four times more often than men.
LLMs also are noticed to show political bias with a study on the 2024 European Parliament elections found a stark bias in ChatGot in favour of the left-wing aligned and centrist parties.
A study by MIT, which found that facial recognition systems were significantly less accurate for darker skin tones compared to lighter ones. This is not because the AI “chose” to discriminate. It is because the data it learned from was not representative enough.
Biased data in. Biased output out.
This is the simplest way to understand AI bias. And yet, it is often ignored in conversations about unbiased AI.
The idea of unbiased AI is appealing. It suggests that technology can rise above human flaws. It suggests that artificial intelligence can deliver pure, objective truth. But this idea is misleading.
AI does not exist outside human systems. It is built by humans. It is trained on human data. It is shaped by human decisions. Every stage of AI development introduces potential bias. From what data is collected, to how it is labeled, to how models are trained, every step influences the final output.
So when we talk about unbiased AI, we are often ignoring the entire pipeline that creates artificial intelligence.
Unbiased AI is not the default. In many cases, unbiased AI is not even achievable. What we can aim for instead is reduced bias, transparent bias, and accountable AI systems.
Not all AI systems behave the same way. This is where AI model comparison becomes important. Different companies build artificial intelligence systems with different priorities, different datasets, and different design philosophies.
This is why there is no single “objective AI.” There are multiple AI systems, each reflecting different slices of the world.
One of the reasons the myth of unbiased AI persists is because AI sounds confident. It delivers answers in a clear, structured, and authoritative way. There is no hesitation. No uncertainty in tone. This creates an illusion of accuracy. Researchers warn that the veneer of objectivity when it comes to technological tools could make people even less willing to acknowledge the problem with biased outputs, making the illusion of neutrality an even darker aspect of AI
Artificial intelligence is designed to produce the most probable answer, not the most truthful one. It predicts what is likely based on patterns in data. This is a key distinction in understanding machine learning.
AI does not verify truth in the human sense. It calculates likelihood.
A study conducted by MIT in 2026 revealed that LLMs systematically ignore the information in the middle of long documents often only focusing on the starting and the end. 36% companies also reported negative impacts from AI bias in 2024, including loss in revenue, Lost customers and employee attrition, making AI bias not just a social issue but a growing liability in legal, research based or business fronts.
Thus, it is to note that a tool that has become a part and parcel of life might just be slipping in machine learning bias into our brains through a lens of objectivity.
In today's age using AI tools is easy. Questioning AI outputs is harder. But it is necessary.
The future of AI will not be defined by how advanced these systems become. It will be defined by how critically we engage with them. Because artificial intelligence is not neutral. It is not objective. It is not free from bias.
Artificial intelligence is trained. Artificial intelligence is shaped. Artificial intelligence reflects patterns.
So the next time you use AI, ask yourself one simple question: Is this the truth?
