Summary of the AI considerations framework
| Consideration | Descriptor questions |
|---|---|
| Context | What is the reason(s) for using AI in this context? How was the problem addressed previously? What value does AI add here? |
| Data | What data do we need to access to train the algorithm? Is it representative of the intended population? |
| Validation | Is the algorithm valid across geographies and over time? Is it generalisable across populations? |
| Implementation | How will the model work ‘in practice’? What are the scope and limitations of using this algorithm? What training is needed for end users? |
| Surveillance | How is the performance of the algorithm monitored over time? How can users report errors with model performance? |
| Success metrics | Is the algorithm working as intended? Are we confident the benefits outweigh the costs (eg clinical, health economic and planetary)? |
| Ethics and governance | What safeguards are in place to protect against algorithmic bias? Is the algorithm transparent and explainable? |
| Managing change | What else needs to occur alongside introducing a new algorithm to achieve system-wide improvement? |