Risk mitigation roadmaps

Our mission at Holistic AI is to reduce risks connected to AI and data projects.

We introduce here the risk mitigation roadmaps, a set of guides that will help you mitigate some of the most common AI risks.

A roadmap explains outlines the technical risk and presents potential solutions, usually composed of two or more steps. The roadmaps are also accompanied by tutorials and examples in the form of Jupyter Notebooks.

How to navigate the roadmaps

We can think of AI risks as being divided in 5 different areas:

• Efficacy: Risk that the system underperforms relative to its use-case.

Improving generalization through model validationHyperparameter Optimisation

• Robustness: Risk that the system fails in response to changes or attacks.

Handling dataset shiftAdversarial training for robustness

• Privacy: Risk that the system is sensitive to personal or critical data leakage.

Data Minimization techniques

• Bias: Risk that the system treats individuals or groups unfairly.

Measuring Bias and DiscriminationMitigating Bias and Discrimination

• Explainability: Risk that an AI system may not be understandable to users and developers.

Documentation for improved explainability of Machine Learning models

Extracting Explanations from Machine Learning Models