This is a great reminder that most “helpful” AI is just optimized conformity.
When models suggest edits, they’re not offering insight — they’re offering what’s safest, most average, most familiar to the dominant culture. And that’s often Western, white, male-coded language that reads as “neutral” because it’s historically overrepresented in training data and platform norms.
This isn’t just about grammar or clarity. It’s about whose voice gets flattened and whose story gets smoothed out until it sounds like a TED Talk.
We should stop thinking of AI as neutral by default. The bias isn’t a bug — it’s baked into the system of reinforcement learning and feedback loops that reward comfort over challenge, safety over truth, sameness over difference.
Anyone here doing work to counteract this? How do you keep LLMs from deradicalizing or deracializing your writing?
When models suggest edits, they’re not offering insight — they’re offering what’s safest, most average, most familiar to the dominant culture. And that’s often Western, white, male-coded language that reads as “neutral” because it’s historically overrepresented in training data and platform norms.
This isn’t just about grammar or clarity. It’s about whose voice gets flattened and whose story gets smoothed out until it sounds like a TED Talk.
We should stop thinking of AI as neutral by default. The bias isn’t a bug — it’s baked into the system of reinforcement learning and feedback loops that reward comfort over challenge, safety over truth, sameness over difference.
Anyone here doing work to counteract this? How do you keep LLMs from deradicalizing or deracializing your writing?