"grad-ai-ethics"

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

Assumptions

IRON LAW: AI systems encode the VALUES of their designers and training
data — there is no value-neutral AI, and "optimizing for accuracy"
without fairness constraints reproduces existing inequalities.

Key assumptions:

1. All datasets reflect historical decisions and biases — "ground truth" is socially constructed
2. Fairness has multiple, mathematically incompatible definitions — choosing one is a value judgment
3. Transparency and explainability are not the same — a system can be transparent (open code) but not explainable (no one understands why it decided X)
4. Accountability requires clear chains of responsibility from developer to deployer to affected party

Methodology

Step 1: Map the AI System and Stakeholders

Identify the AI system's function, decision domain, affected populations, and the power asymmetry between system operators and subjects.

Step 2: Assess Fairness

Evaluate using multiple fairness definitions: