AI-ethics (datacamp course)

Applying AI ethics

• Fairness

• Ensuring to have the diverse database, no bias

• Accountability

• Clear responsibility defined for each AI system’s outcome

• When there is a malfunction, who is the responsible for that malfunction

• Transparency

• Make the AI system explainable and understandable

• Commitment

• Ethical adherence builds trust, and mitigates risks

Privacy-personalization

• Personalization can compromise the privacy

Transparency-complexity

• Easy model – easy to understand but less accuracy

• Complex model – hard to understand but improve the accuracy

Autonomy-control

• Which one should be set up as the first priority?

• Control compromises autonomy