Ask Google, “Who is the top goal scorer in football for their country?” Before the page even loads, you already know what name you expect to see. Most of the time, Google delivers exactly that, Cristiano Ronaldo. And yes, with over 143 international goals for Portugal, the answer feels familiar. Confident. Impressive. Case closed.

Except… not really.

If the question is simply “who is the top goal scorer?” without mentioning men or women, why does the answer default to men’s records? The actual top international goal scorer in football is Christine Sinclair, with 190 goals for Canada. Yet her name often appears several scrolls below, quietly waiting for someone curious enough to keep looking, or it appears after adding the word “women.” This is not a football problem orsports problem. And this is definitely not a men vs women problem. Rather, it is a data problem and a user problem.

Search engines do not think, they predict. They are trained on what we click, what we search and how long we stay on a page. If millions of users repeatedly click on men’s football content, the algorithm learns a simple lesson. Over time, it stops asking and starts optimising for what is popular.

From a data perspective, this is a classic case of biased input leading to biased output. The algorithm is not wrong; it is just extremely obedient. It reflects our internet habits.. These systems can create a “bubble” in which we see only the most popular opinion andor the loudest voices, which may obscure the facts. If we always let a computer give us the easy, popular answer, we risk losing our critical thinking skills and our ability to have deep, meaningful conversations. With the election around the corner and as a responsible citizen, r, this habit can eventually lead us to treat the election and our fellow citizens as simple data points. It prevents individuals from exploring the full story behind their candidates and their plans.

To fix these biases,, we must create tools that do not just guess based on past clicks but instead check a “map of fact” to find the real truth. This involves using “Knowledge Graphs” to connect different pieces of information so the computer can present a comprehensive picture rather than merely showing what is famous or viral. By using a method called “Retrieval Augmented Generation”, we can make sure an AI looks at verified sources

and official records before it answers. My goal is to study how we can use these advanced data methods to ensure that technology helps citizens find accurate information instead of just repeating the same popular biases.

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