Ethics, Bias, and Safety

Ethics, Bias, and Safety

Overview

ML systems can amplify bias and cause harm if not designed carefully.

Considerations

• Bias in data and labels
• Privacy and PII handling
• Safety filters and abuse prevention

Checklist

• Document assumptions and limitations
• Get stakeholder feedback early

Resources

• Partnership on AI
• NIST AI Risk Management Framework