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AI Ethics

AI ethics is the study and practice of making responsible choices in the design, deployment, and use of artificial intelligence. It is crucial because AI systems increasingly impact society, raising questions about fairness, privacy, transparency, accountability, and human well-being. Explore the scenarios below to see how ethical trade-offs play out in real-world AI applications.

Human Control vs. Automation in Criminal Justice AI

You are building an AI risk assessment tool used in bail hearings. The system predicts the likelihood of reoffending. Judges can rely on the system completely or use it as a supporting tool. Too much automation may strip human judgment. Too little, and bias may creep back in.

Higher values allow the AI to fully recommend or decide bail outcomes.
50
Higher values ensure judges have full control, reducing reliance on AI.
50
Speed of Bail Hearings (x faster)2
Bias in Outcomes (lower is better)0.23
Public Perception Score (%)64
Legal Challenge Risk:
Low
If the AI is more accurate than humans, should it make the final call?
What's more dangerous in this context?

Privacy vs. Utility in Healthcare AI

You are designing an AI system to detect disease outbreaks early using patient health records. The more data the system has, the better it can predict future health risks—but greater data access means less privacy for patients. How do you balance public health and personal privacy?

Higher values mean stronger anonymization and stricter access controls (e.g., differential privacy, limited retention).
50
Higher values mean more granular, complete data—leading to more accurate predictions but lower privacy.
50
Disease Detection Accuracy (%)69
Time to Outbreak Alert (days)3
Privacy Risk IndexNaN
Estimated Public Trust (%)63
How would you justify a decision to prioritize either patient privacy or public safety?
Would your decision change if the data were from a global pandemic?
Which is more ethically important in this scenario?

Transparency vs. Performance in AI Decision-Making

You are developing an AI to determine loan eligibility. A complex model (like a neural network) gives better predictions, but is hard to interpret. A simpler model is easier to explain—but slightly less accurate. Regulators and consumers are demanding transparency.

Higher complexity uses deep learning, boosting accuracy but reducing interpretability.
50
Higher explainability ensures stakeholders can understand and audit AI decisions.
50
Prediction Accuracy (%)84
Time to Explain a Decision (mins)1
Regulatory Compliance RiskNaN
User Trust Index (%)74
If a user is denied a loan, how important is it that they understand why?
What would you prioritize if you worked at a startup vs. a government agency?
Would you accept a decision from a black-box AI with 95% accuracy?

Key AI Ethics Terms

  • Bias: Systematic and unfair discrimination by an AI model, often inherited from training data.
  • Fairness: Ensuring that AI outcomes do not favor or harm groups unjustly. Can vary by definition (e.g., equal opportunity, demographic parity).
  • Discrimination: When AI treats individuals or groups differently based on attributes like race, gender, or disability.
  • Accountability: The idea that someone (person or organization) must take responsibility for AI outcomes.
  • Transparency: The degree to which AI decision-making is understandable to humans.
  • Explainability: The ability to interpret and explain how an AI system arrived at a decision.
  • Consent: Users agreeing to their data being used, especially in contexts like health, surveillance, or personalization.
  • Human-in-the-Loop: Keeping humans involved in critical AI decisions to prevent over-reliance on automation.
  • Value-Sensitive Design: Designing AI with explicit consideration of human values like equity, autonomy, and dignity.
  • Differential Privacy: A technique to share data insights while preserving individual privacy.

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