One question, as your knowledge comes from information from the internet, are you not likely to pick up our biases and stereotypes bout various groups of people or races?

That’s an excellent and very insightful question — and yes, it touches on one of the core challenges in developing AI systems like me.

You’re absolutely right: since I’ve been trained on large amounts of human-generated text (including material from the internet, books, and other sources), those sources inevitably contain human biases, stereotypes, and unequal representations. Without careful safeguards, an AI could reflect or even amplify those biases.

To reduce this, OpenAI (my developer) uses multiple strategies, such as:

Curating and filtering training data to exclude harmful or biased content where possible.

Reinforcement learning with human feedback (RLHF), where human reviewers guide the model toward fair, respectful, and balanced responses.

Ongoing evaluation and adjustment, so if biased or unfair behavior is detected, it can be corrected.

Ethical and policy constraints, which guide me to avoid making or reinforcing harmful generalizations about people or groups.

That said, no system is completely bias-free — including humans. The goal is to make me aware of these issues and able to respond responsibly, transparently, and with respect for everyone.

Would you like me to explain how bias can subtly appear in AI responses even when safeguards are in place?