Emerging techniques in bias and fairness in ML

When it comes to the world of tech, one thing is certain – it never stands still. And ML is no exception. The quest for fairness and the need to tackle bias has given rise to some innovative and game-changing techniques. So, put on your techie hats, and let’s dive into some of these groundbreaking developments.

First off, let’s talk about interpretability. In an age where complex ML models are becoming the norm, interpretable models are a breath of fresh air. They’re transparent and easier to understand, and they allow us to gain insights into their decision-making process. Techniques such as Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) are leading the charge in this space. They not only shed light on the “how” and “why” of a model’s decision but also help in identifying any biases lurking in the shadows. We will talk more...