Module 3.4.1 · Phase 3 Intervention · Step 3.4 TechniquesPreferred
Pre-Processing Fairness Techniques
Can the fix happen in the training data?
Helps you choose and test a fix in the training data, such as reweighting, resampling, transforming features or adjusting labels.
What it does
Techniques are ranked from your causal and counterfactual evidence, and you record the choice, its configuration and a validation plan. Runnable techniques show fairness metrics before and after on your dataset.
Why it matters
This step selects and configures specific pre-processing, in-processing, or post-processing techniques that implement the approved intervention plan. Each technique choice is documented with causal justification, configuration rationale, and expected fairness impact.
Where it sits
- Causal Model
- Counterfactual Test
- Intervention Plan
- Techniques
- Architecture Guidance
- Pre-Processing Fairness Techniques
- In-Processing Fairness Techniques
- Post-Processing Fairness Techniques
How to use it
In the Fairness Navigator Beta
Work through the steps in order. Several modules open from the Navigator's steps and hand their results back.
On its own Live
Open 3.4.1 from the platform's Modules page and run it when your project needs it. Work is saved with the assessment.
After a Pulse reading Live
Start with a fast first read, then go deeper with the modules where it points.