Human evaluators are at the core of RLHF, providing feedback that the model uses to adjust its behavior. However, this feedback is inherently subjective. Each evaluator brings their own set of cultural perspectives, personal experiences, and biases to the table. For instance, two evaluators from different cultural backgrounds might provide different feedback on the same model output, leading to inconsistencies. If not carefully managed, these subjective judgments can introduce biases into the model, causing it to reflect the particular perspectives of the evaluators rather than a more balanced view.
Humans are not always consistent in their feedback, particularly on subjective matters. What one person considers appropriate or correct might differ significantly from another's opinion. This inconsistency can confuse the model, leading to unpredictable outputs or reinforcing biased behaviors. The model may struggle to learn a clear, unbiased pattern if the feedback it receives is too varied.
Bias in AI models often stems from biased training data. When using RLHF, if the training data already contains biases, there's a risk that human feedback will reinforce these biases rather than correct them. For example, if the model's outputs reflect gender stereotypes and human evaluators unintentionally reinforce these through their feedback, the model's behavior may become skewed and less aligned with equitable outcomes.
Collection
[
|
...
]