The AI product development lifecycle traces how data becomes an AI-supported decision, from preparation and training through deployment, use, evaluation, and retirement. It is not only a technical pipeline: privacy, bias, data quality, transparency, and accountability require attention at every stage. section 3.18
Responsible lifecycle management requires organizations to:
- Set standards for training data and model management. section 3.18
- Build transparency and accountability into the pipeline, rather than adding them only at user interaction. section 3.18
- Treat legal compliance as a baseline while addressing broader systemic risks. section 3.18
