Ethical Considerations in AI Development Lifecycle: From Data Collection to Deployment

Intermediate

Ethical AI development spans all phases:

Data Collection: Ensure data is representative and obtained ethically.
Model Development: Avoid embedding biases; validate model fairness.
Deployment: Monitor for unintended harms; provide transparency.
Post-Deployment: Continually audit and update systems based on real-world feedback.

Analogy: Like building a bridge, ethical AI requires careful planning, construction, and maintenance to ensure safety and durability over time.