Ethics in Machine Learning: More Than Just an Afterthought

It’s easy to get caught up in the technical coolness of ML and forget about the human impact. I’ve had to sit in meetings where we realized our training data was biased against certain demographics. It’s a heavy responsibility. The algorithm you build might affect someone’s life—whether it’s hiring, policing, or healthcare. We have a duty to audit our data, test for fairness, and think about the consequences. It’s not just about avoiding bad PR; it’s about doing the right thing.