In recent days, several posts on social media have claimed that artificial intelligence tools such as Grok and AskPerplexity are producing politically slanted answers — particularly those that appear critical of the BJP, the Government of India, or right-wing public figures.
Some users have even suggested that “AI has turned anti-Modi.”
But experts say such claims misunderstand how AI actually works — and that the real issue may lie not in the machines themselves, but in how humans shape and feed them.
AI Reflects the Internet — It Doesn’t Think for Itself
Artificial intelligence does not form opinions, take political positions, or “decide” what is right or wrong.
Most large language models are trained on enormous datasets drawn from the open internet — news reports, blogs, forums, and social media posts. These sources are neither ideologically neutral nor evenly distributed.
If negative or critical narratives about a government or political figure dominate the online space, an AI trained on that material will mirror those trends.
It doesn’t mean the algorithm is “anti-government”; it simply means it is echoing the data it has seen most often.
When Repetition Becomes Reality
Information on the internet tends to reward frequency, not necessarily accuracy.
Narratives that are shared repeatedly — whether factual or not — become more visible to search engines and, by extension, to AI models that learn from them.
This creates what analysts call a “data imbalance”: the louder a viewpoint becomes online, the more likely it is to appear in an AI-generated summary or explanation.
As a result, even misinformation can sometimes surface as a “majority view,” while factual corrections or alternative perspectives remain under-represented.
The Human Factor Behind AI Bias
Every AI system is built, trained, and fine-tuned by humans.
Developers decide which data sources to include or exclude, which guidelines to apply, and how responses should be filtered.
Consequently, any human bias — conscious or unconscious — can subtly influence the model’s output.
Bias, therefore, does not emerge from the code alone; it stems from the data pipelines, labeling processes, and editorial decisions that shape AI behavior.
In short, AI bias is often a reflection of human bias, magnified by scale and automation.
The Politics of Information Saturation
Online ecosystems are not neutral environments.
Political groups, advocacy networks, and organized digital communities across the spectrum actively shape narratives by producing and amplifying large volumes of content.
When one side of the political divide consistently dominates the online conversation, that imbalance can influence what AI models learn to prioritize.
In India’s case, years of highly polarized online debate have created a skewed data environment, where both pro- and anti-government material circulate in unequal proportions.
AI tools trained on this uneven terrain naturally reproduce its asymmetries.
Machines Aren’t the Enemy — Misinformation Is
Blaming AI for perceived political bias risks missing the core problem: the quality and balance of information available online.
If misinformation, sensational headlines, or partisan narratives dominate the digital space, algorithms will absorb and replicate that noise.
Ensuring fairer, more factual AI outputs therefore requires improving the informational ecosystem, not attacking the technology itself.
Promoting verified data, encouraging diverse viewpoints, and reducing online echo chambers will help future AI systems deliver more balanced perspectives.
A Mirror, Not a Mind
AI does not hold political beliefs — it reflects the data that society produces.
If that reflection appears distorted, the fault lies not in the machine but in the mirror’s surface: the uneven, polarized, and often manipulated digital world it draws from.
The debate over “AI bias” ultimately reveals more about human behavior, media ecosystems, and online discourse than about artificial intelligence itself.
To make AI fairer, we must first make our information environments healthier.
