Alongside typical validation, our AI/ML validation contains the following specific components:
- Data policy: availability, quality, adequacy/representativeness, biases.
- Actual modelling choices (choice of the ML model and possible alternatives)
- Outcomes’ interpretability
- Potential biases towards protected attributes and their mitigation. We can help you to find a right balance between fairness and model performance.
- Hyperparameters choice and sensitivity
- Benchmarking to industry-standard (classification, regression) models
- Model deployment and operational assessment