AI Ethics

Overview

AI ethics examines the moral dimensions of artificial intelligence systems, centered on four pillars: fairness, accountability, transparency, and privacy (FATE). As AI systems increasingly make consequential decisions, they inherit and amplify the biases embedded in training data and design choices. Ethical AI requires proactive identification of bias, explainability mechanisms, clear accountability structures, and privacy protections.

When to Use

• Auditing an AI system for fairness before or after deployment
• Designing bias mitigation strategies for machine learning pipelines
• Evaluating explainability requirements for different stakeholder audiences
• Assessing regulatory compliance (EU AI Act, GDPR, sector-specific requirements)

When NOT to Use

• When the question is purely about model performance without ethical dimensions
• When analyzing non-AI automation or rule-based systems with full transparency
• When the focus is on AI technical architecture without deployment context