Lesson 6 of 8Article17 min
AI Ethics, Bias, and Responsible Use
AI Ethics, Bias, and Responsible Use
Common AI risks
- Bias from unrepresentative training data.
- Privacy leakage and sensitive data exposure.
- Lack of transparency and explainability.
- Automation without accountability.
Responsible AI checklist
- Define acceptable error and harm thresholds.
- Track fairness metrics across demographic groups.
- Use human review for high-impact decisions.
- Document model limits and failure modes.
Ethics is not a one-time review; it is a lifecycle process from data collection to monitoring.