AI Ethics: Building Responsible AI Applications

With great power comes great responsibility.

Core Principles

1. Transparency

Label AI content, explain decisions, share training data info.

2. Fairness

Test for bias, monitor disparities, correct issues.

3. Privacy

Minimize collection, secure data, give users control.

4. Safety

Filter content, monitor misuse, enforce policies.

Implementation

Content Moderation

Check inputs and outputs for harmful content. Log violations.

Bias Testing

Test across demographic groups. Measure quality, representation, stereotyping.

Audit Logging

Record decisions with timestamps and confidence scores.

Avoiding Pitfalls

Policy Framework

Every AI app needs:

Building ethical AI isn't about avoiding harm—it's about actively creating systems that benefit everyone.