The AI-RFX Procurement Framework
Raising the bar for AI safety, quality and performance in industry
The AI-RFX procurement framework has been put together by a group of domain experts. Its purpose is to ensure best practices in industry during the procurement, design, development and deployment of machine learning systems in industry.
This procurement framework goes beyond the machine learning algorithms. It provides a method to assess the maturity level of the technical infrastructure and processes around the algorithms themselves, by using our Machine Learning Maturity Model.
The Machine Learning Maturity Model
The Machine Learning Maturity Model is a set of criteria which ensures that the core technical infrastructure and the right processes are in place. It provides the ability for key industry practitioners to set a bar for quality, safety and performance when procuring machine learning solutions. You can request to download a copy of the AI-RFX Framework through the contact form below, which contains this document as well.
The model was designed using the Responsible Machine Learning Principles, and consists of the following 8 assessment criteria:
| Number | Assessment Criteria | Responsible ML Principle |
|---|---|---|
| #1 | Practical benchmarks | Principle #6: Practical accuracy |
| #2 | Explainability by justification | Principle #3: Explainability by justification |
| #3 | Infrastructure for reproducible operations | Principle #4: Reproducible operations |
| #4 | Data and model assessment processes | Principle #2: Bias Evaluation |
| #5 | Privacy enforcing capabilities | Principle #7: Trust by privacy |
| #6 | Operational process design | Principle #1: Human Augmentation |
| #7 | Change management capabilities | Principle #5: Displacement strategy |
| #8 | Security risk mitigations | Principle #8: Security risks |
The Institute for Ethical AI & Machine Learning