- Create a standardized approach for developing and deploying machine-learning.
- Identify prejudices that exist in processes for collecting and processing data.
- Test for unwanted biases that may be embedded in algorithms.
- Invest in explainability.
- Monitor performance of algorithms (for bias, predictability and so on).
- Challenge assumptions.