Introduction to Fairness-Aware Machine Learning
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.
What are Embeddings?
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- Occupation terms may reflect outdated gender roles.
The Problem with Biased Embeddings
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.
Detecting and Removing Bias
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.
Conclusion
Embeddings are a crucial part of machine learning models, but they can also perpetuate harmful biases. By understanding how embeddings work and how to detect and remove bias, we can create more fair and accurate models.
Frequently Asked Questions
- Q: What is bias in machine learning?
- A: Bias in machine learning refers to the unfair or discriminatory outcomes produced by a model.
- Q: How can I detect bias in my embeddings?
- A: You can detect bias in your embeddings by analyzing the vector representations and looking for patterns that reflect stereotypes or discriminatory relationships.
- Q: How can I remove bias from my embeddings?
- A: You can remove bias from your embeddings by using techniques such as debiasing algorithms or by retraining your model on a more diverse and representative dataset.
