Ethical AI Principles and Bias Reduction in Algorithmic Decision Making

1. Recognizing Training Data Bias

AI models inherit societal biases embedded in web scrapings. Systematic bias auditing is essential for fair automated decision-making in hiring, finance, and moderation.

2. Model Explainability Tools (SHAP & LIME)

Explainability frameworks allow engineers to quantify how specific input features influence output decisions.

3. Practical Alignment Techniques

Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO) align AI outputs with human safety guidelines.