Guardrails detect and mitigate biased outputs of LLMs by incorporating monitoring tools that analyze generated content for discriminatory language or patterns. These tools assess whether the output reflects unfair stereotypes or prejudices related to gender, race, ethnicity, or other sensitive factors. The guardrails use pre-defined fairness criteria to flag biased outputs and filter them before they reach the end user.
One common technique used by guardrails is the application of fairness guidelines during model training. By analyzing the training data and identifying areas where bias might be present, guardrails can guide the LLM to generate more balanced and neutral content. They can also apply corrections to the output based on recognized bias in the model’s historical responses.
Guardrails are typically adjusted over time, based on feedback and ongoing assessments, ensuring that the model continues to improve its handling of bias in response to new societal concerns or emerging issues in the data. These measures may involve reinforcing the model’s awareness of social biases and guiding its learning toward more inclusive patterns of behavior.