Fairness
Performance by Group
Techniques that estimate or report accuracy, calibration or uncertainty separately for each group so gaps can be seen or closed.
4 techniques
4 techniques
| Goals | Models | Data Types | Description | |||
|---|---|---|---|---|---|---|
| Conformal Prediction | Algorithmic | Architecture/model Agnostic Requirements/black Box | Any | Conformal prediction wraps any trained model so that, instead of a single answer, it returns a set of plausible answers... | ||
| Bootstrap Ensembles | Algorithmic | Architecture/model Agnostic Paradigm/supervised +1 | Any | Bootstrap ensembles estimate how much a model's predictions depend on the particular sample of data it was trained on.... | ||
| Model Cards | Documentation | Architecture/model Agnostic Requirements/black Box | Any | A model card is a short structured document published with a trained model, stating what the model is for and what it... | ||
| Disaggregated Evaluation | Testing | Architecture/model Agnostic Requirements/black Box | Any | Disaggregated evaluation assesses and reports a system's performance separately for each group of people a claim covers,... |
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