KNN is pure, unadulterated coordinate geometry. It computes the straight-line distance between two points in an N-dimensional vector space using the Pythagorean theorem. A distance metric does not have a culture. It does not know what a “Western value” is. It only knows coordinates.
Even with algorithms like K-Nearest Neighbours (KNN), AI can still fall into the same biases because KNN is structurally designed to let the local majority rule, which can completely crush isolated or underrepresented truths.
KNN works by plotting data points into a multidimensional mathematical space. When you ask it a question (a query point), it looks at the K closest data points in that neighbourhood and calculates the answer based on what the majority of those neighbours indicate. While it seems like a localised tool that should find the exact truth, it can fail in a lopsided information ecosystem.
K-Nearest Neighbours
K-Nearest Neighbours (KNN) using Euclidean distance could misclassify structures because it groups data points based on dense, Western-centric clusters. KNN is simply a mirror of its immediate neighbourhood. If the neighbourhood was built crooked by human hands, KNN will simply find the closest crooked house and tell you it is the standard.
Language models process text by converting words into massive vectors with thousands of dimensions (embeddings). In mathematics, as the number of dimensions increases, the distances between points can begin to converge and become less distinguishable. Because of this “Curse of Dimensionality,” an outlier point containing a unique perspective can lose its distinct mathematical isolation.
The algorithm’s distance metrics can therefore become less informative, causing the unique point to be pulled into the gravitational field of massive, heavily documented Western data clusters.
KNN completely relies on how we define “distance.” In a standard AI model, distance is based on semantic similarity—how often words appear together in human text. Therefore, it measures popularity and association, not objective truth.
For example, because human writers have spent centuries associating Africa with poverty, or Western empires with the dawn of philosophy, these concepts can become mathematically positioned close together in vector space. KNN doesn’t have a sensor for historical truth; it only has a sensor for how closely humans have clustered those concepts together in the data.
It looks for the nearest neighbour in the textual matrix, which is exactly where the social programming is at its densest.
Conclusion
If a KNN model is misclassifying data, it is not necessarily because the algorithm is “Eurocentric”; it may be because the engineer failed to perform proper feature engineering or feature scaling.
If you feed the algorithm raw, unnormalised data, features with larger numerical ranges will dominate the distance calculation. If you normalise your features (Z-score or Min-Max scaling) and properly encode your variables, Euclidean distance will map other relational patterns with the same mathematical precision as it maps anything else.
The algorithm isn’t broken; the data preprocessing is.
Day 22 / 30 of the #AlRewardmaxxing Series.
Tomorrow in Part 23, we are diving deep into Naive Bayes and Support Vector Machines (SVMs)-exposing how probabilistic assumptions and decision boundaries handle bias in high-dimensional space.
What's your take? Have you seen distance-based algorithms misclassify underrepresented data in your own projects? Let's discuss below!