Author(s): Sanket Rajaram

Originally published on Towards AI.

Imagine building a machine learning model that performs with excellent accuracy, only to discover it subtly favors certain groups over others.

You check your data, clean your features, even tune your hyperparameters — but the bias remains. That’s because the problem might be deeper — buried right inside your embeddings.

In this article, we’re going to walk through one of the simplest and most effective techniques for detecting and removing bias at the vector level. If you’re someone who works with embeddings — word embeddings, sentence vectors, tabular entity representations — this is your invitation to step into fairness-aware machine learning.

Photo by Steve Leisher on Unsplash

Embeddings are the numerical backbone of your ML pipeline. They capture semantics, similarity, and structure. But they also capture something more dangerous: bias.

If you’re using pretrained embeddings or training your own on historical data, chances are your vectors have absorbed patterns that reflect stereotypes:

“Doctor” might lean closer to “he” than “she”.“Leader” may drift toward “white” in racially-skewed corpora.Occupation terms may reflect outdated gender roles.

These patterns aren’t just inconvenient — they’re harmful. They silently alter your model’s worldview.

Even though we’ve been writing code and plotting vectors, there’s solid science behind it. Here’s the real methodology that powers the… Read the full blog for free on Medium.

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Published via Towards AI