AI Ethics 101
AI is powerful. Power requires responsibility. Here are the ethical considerations everyone using AI should understand.
The Big Questions
1. Bias and Fairness
The Problem: AI learns from human-generated data. Human data contains human biases.
Real Examples:
- Resume screening AI that favored men (trained on historical hiring data)
- Facial recognition that performed worse on darker skin tones
- Loan algorithms that discriminated against certain zip codes
What to Consider:
- What data was the AI trained on?
- Whose perspectives are represented?
- Who might be harmed by errors?
2. Transparency and Explainability
The Problem: Most AI systems are "black boxes"—we can't see how they reach conclusions.
Why It Matters:
- How do you appeal an AI decision that affects you?
- Can you trust a diagnosis you don't understand?
- How do you fix bias you can't identify?
The Tradeoff: More powerful models are often less explainable. The best-performing AI is often the hardest to interpret.
3. Privacy
The Problem: AI needs data. Often, lots of personal data.
Concerns:
- Training data may include private information
- AI can infer sensitive information from innocuous data
- Data collected for one purpose may be used for another
Questions to Ask:
- What data does this AI collect?
- Who has access to it?
- Can I opt out?
4. Job Displacement
The Reality: AI will change work. Some jobs will disappear. Others will transform. New ones will emerge.
The Nuance:
- Automation has always changed jobs (ATMs didn't eliminate bank tellers)
- The question is speed—how fast can workers adapt?
- Benefits and costs won't be distributed evenly
What's Different: AI affects cognitive work, not just physical tasks. Writers, lawyers, programmers—no one is fully immune.
5. Misinformation
The Problem: AI can generate convincing fake content at scale:
- Deepfake videos
- Synthetic articles
- Fake social media accounts
The Challenge: Creating fakes is easier than detecting them. The asymmetry favors bad actors.
Implications:
- Harder to trust what you see
- Erosion of shared reality
- Democracy and journalism at risk
Responsible AI Use
For Individuals
Do:
- Verify AI outputs, especially for important decisions
- Consider who might be affected by how you use AI
- Be transparent when AI generated your content
- Report harmful outputs to developers
Don't:
- Use AI to deceive or manipulate
- Trust AI blindly for consequential decisions
- Share others' private information with AI
- Assume AI is neutral or objective
For Organizations
Do:
- Audit AI systems for bias
- Maintain human oversight for high-stakes decisions
- Be transparent about AI use
- Consider impact on employees and society
Don't:
- Deploy AI without testing for harms
- Hide AI decision-making from affected parties
- Ignore feedback about problems
- Prioritize efficiency over ethics
The Current Regulatory Landscape
EU AI Act (2024):
- Risk-based approach
- Strict rules for "high-risk" AI (hiring, credit, law enforcement)
- Transparency requirements
- Heavy fines for violations
US Approach:
- Sector-specific guidelines
- Executive orders on AI safety
- No comprehensive federal law (yet)
China:
- Strict content rules
- Algorithm registration requirements
- Focus on social stability
The Alignment Problem
The deepest ethical question: How do we ensure AI systems do what we actually want?
The Challenge:
- We can't perfectly specify human values
- AI optimizes for what we measure, not what we mean
- Powerful AI pursuing wrong goals = disaster
Current Approaches:
- RLHF: Training AI on human preferences
- Constitutional AI: Built-in principles
- Interpretability research: Understanding what AI is "thinking"
What You Can Do
- Stay informed — AI is evolving fast
- Think critically — Question AI outputs and applications
- Speak up — Report problems and advocate for responsible use
- Vote and engage — Policy matters
AI ethics isn't just for researchers and policymakers. Everyone using AI has a role to play.
Next up: Understanding Tokens — The basic unit of AI language