AI capabilities come with responsibilities. Deploying AI in production requires thinking beyond accuracy to consider fairness, transparency, and safety.
Responsible AI Principles
- Fairness - Don't discriminate
- Reliability - Work consistently
- Privacy - Protect data
- Inclusiveness - Work for everyone
- Transparency - Explain decisions
- Accountability - Have human oversight
Bias Detection
from fairlearn.metrics import MetricFrame
mf = MetricFrame(
metrics=accuracy_score,
y_true=y_test,
y_pred=predictions,
sensitive_features=demographics
)
print(mf.by_group)
Explainability
Users deserve explanations:
- Why was this content recommended?
- Why was this application denied?
- What factors influenced this score?
Red Teaming
Before deployment, actively try to break your AI:
- Prompt injection attacks
- Jailbreaking attempts
- Edge cases and adversarial inputs
Find the problems before your users do.