AI Ethics

Definition

The field of study and practice concerned with ensuring that AI systems are designed, deployed, and governed in ways that are fair, safe, transparent, accountable, and aligned with human values and dignity.

Why It Matters

AI systems make or influence consequential decisions at scale — hiring, lending, sentencing, healthcare, surveillance. When these systems embed bias, optimize for the wrong objectives, or concentrate power dangerously, the harms multiply faster than any single human could cause.

Core Problems

Algorithmic Bias (Weapons of Math Destruction)

• Models trained on historical data perpetuate historical discrimination
• Opacity (black box models) prevents accountability
• Scale amplifies harm — a biased human decides a few cases; a biased algorithm decides millions
• The people harmed are typically those least able to challenge the system

Misalignment (The Alignment Problem, Human Compatible)

• Systems optimized for measurable proxies often diverge from what we actually want
• The more capable the system, the more dangerous misalignment becomes
• The solution is not better constraints but systems that remain uncertain about values and defer to human correction

Power Concentration (The Coming Wave, Atlas of AI)

• AI capabilities are asymmetrically distributed — those who control AI gain disproportionate leverage
• AI built on extracted data from workers, communities, and natural resources without fair compensation
• Democratic governance of AI requires institutional capacity that currently doesn’t exist

Key Principles

1. Transparency: Systems should be explainable enough for accountability
2. Fairness: Outcomes should not discriminate unlawfully or unjustly across groups
3. Safety: Systems should be controllable and correctable
4. Human dignity: Persons should not be reduced to data points in systems that determine their life chances
5. Accountability: Someone must be responsible for what AI systems do

Related Concepts

• AI Safety — the technical and governance challenge of keeping AI systems under human control
• Human-AI Collaboration — ethics shapes how collaboration should be structured
• Human Dignity — the philosophical ground for why AI ethics matters
• Large Language Models — the technology raising most of the current ethical questions
• Surveillance Capitalism — the economic model driving many AI ethics problems

Key Books

• Weapons of Math Destruction — the essential account of algorithmic bias and harm at scale
• The Alignment Problem — why getting AI to do what we want is technically hard
• Atlas of AI — AI’s costs in labor, environment, and political power
• Human Compatible — the technical solution: design AI to be uncertain about and deferential to human values
• The Coming Wave — the governance challenge; why containment matters and why it’s so hard