AI Governance · Fairness

AI Bias & Fairness Statement

AI systems are trained on human data and reflect human patterns — including biases. We design prompts and tools with this in mind.

Last updated: August 9, 2026

1. Model bias

Large language models can encode stereotypes related to gender, race, age, disability, religion, nationality, accent, and many other attributes. They can also amplify cultural assumptions baked into training data.

2. Fairness in practice

We design prompts to be neutral, professional, and inclusive. We avoid prompts that ask models to make predictive judgments about people in ways that are unreliable or unjust.

3. Inclusiveness

We encourage outputs written in clear, accessible language and free of demeaning, exclusionary, or discriminatory framing.

4. Human oversight

Final decisions about people — hiring, performance, grades, treatment, lending, sentencing — must remain with qualified humans. AI may inform; it must not decide. Where outputs are used in such contexts, review them for biased phrasing, assumptions, and recommendations before relying on them.