What Ethical AI Means in Practice

Bias detection, transparency, consent management, and fairness metrics.

Where Teams Go Wrong

Ethics is often bolted on after launch—too late to fix core flaws.

Best Practices

• Embed ethical reviews into sprints

• Train models on inclusive datasets

• Make all AI decisions auditable

• Explain how AI makes key decisions

• Keep human override available in critical paths

Key Takeaways

• Scalable AI needs scalable ethics

• Transparency drives user trust

• Post-launch fixes are expensive

• Responsible design is long-term product insurance