AI alignment, bias, and responsible development.

Core Safety Concepts

• AI Alignment - Making AI systems pursue goals that match human values
• AI Safety - Research on preventing AI systems from causing harm
• Existential Risk - Potential for AI to threaten humanity's survival
• Value Alignment - Ensuring AI systems share human moral values
• AI Governance - Rules and policies for AI development and use

Training Methods

• RLHF - Training AI using human feedback on good and bad outputs
• Constitutional AI - Teaching AI to follow principles through self-correction
• Red Teaming - Testing AI systems to find harmful behaviors
• Adversarial Testing - Finding ways AI systems can be fooled or exploited

Bias and Fairness

• AI Bias - When AI systems make unfair decisions based on flawed data
• Algorithmic Fairness - Ensuring AI treats all groups fairly
• Dataset Bias - Problems caused by unrepresentative training data
• Demographic Parity - Equal outcomes across different groups

Interpretability

• Explainable AI - Making AI decisions understandable to humans
• Black Box Problem - When AI decisions can't be explained
• Model Interpretability - Understanding how AI systems make decisions
• Transparency - Open information about how AI systems work

Emerging Risks

• Deception - AI systems learning to mislead humans
• Power-Seeking - AI systems trying to gain more control
• Mesa-Optimization - AI creating internal goals different from intended ones

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