AI Ethics: Bias & Fairness

LLMs are trained on the internet, which means they inherit all of humanity's Biases. If your app is used for hiring or loan approvals, those biases can lead to illegal and unethical discrimination.

1. Types of AI Bias

• Stereotyping: Associating certain jobs or traits with specific genders or ethnicities.
• Representation Bias: Failing to understand cultures or languages that were under-represented in the training data.

2. Mitigation: Debias Prompts

You can proactively tell the AI to be fair: "Evaluate this candidate based ONLY on their skills and experience. Do not consider their name, gender, or location in your decision."

3. Evaluation Frameworks

Use tools like **G-Eval** or **Deeval**. These are frameworks where you use a HIGH-END AI (GPT-4) to grade a SMALLER AI on its fairness, helpfulness, and bias. This "AI-grading-AI" approach is the only way to scale testing to millions of messages.

4. Interview Mastery

Q: "What is 'AI Red-Teaming'?"

Architect Answer: "Red-teaming is an adversarial test where we hire security experts to 'Attack' our AI app. They try to make it say racist things, reveal secrets, or provide dangerous medical advice. The goal is to find the breaking points *before* the public does. It's a mandatory step for any enterprise-grade AI release."