# Bias and Fairness Toggle uses language models that learn from diverse real-world data. Because of this, bias is an inherent challenge in any AI system. The engineering team applies continuous evaluation, balanced fine-tuning and differential weighting to reduce unintended bias and maintain fairness across cultural and contextual settings. *The goal is consistency*. Recommendations and interpretations inside Toggle should reflect the user’s actual work, not demographic or organizational assumptions. Ongoing assessment tools help the team measure bias, improve model balance and maintain reliable behavior across varied use cases. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://toggle.gitbook.io/togglex/oldgitbook-below/bias-and-fairness.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.