Mitigating Algorithmic Bias and Tackling Model and Data Drift
If you’re playing in the arena of machine learning (ML) and data science, you’re going to run into some hurdles. You can count on meeting two challenges: algorithmic bias and model and data drift. They’re like the tricky questions in a pop quiz – you might not see them coming, but you’d better be prepared to handle them.
Algorithmic bias can creep into our models, and when it does, it’s not a good look. It can lead to unfair results, and, quite frankly, it’s just not cool. But don’t worry – we’re going to tackle it head on and talk about ways to mitigate it.
Even if we consider bias, over time, changes can happen that make our models less accurate. It’s like when your favorite shirt shrinks in the wash – it’s not the shirt’s fault, but it doesn’t fit like it used to. The same happens with our models. They may have...
