Implementation of "Operationalizing Individual Fairness with Pairwise Fair Representations"
Link to Google Slides presentation
| Dataset | Number of Records (Paper) | Number of Records (Our Implementation) | Number of Features (Paper) | Number of Features ( Our Implementation ) | True Rank | Protected Attribute |
|---|---|---|---|---|---|---|
| Compas | 8803 | 6903 | 429 | 456 | 117 | Race_African-American |
A list of packages required to run is mentioned in requirements.txt file.
Reproduced Results :
| Original Representation | Pairwise Fair Representation (Gamma = 0.5) |
|---|---|
| Accuracy: 69.56% | Accuracy: 66.01% |
| ROC-AUC score: 69.04% | ROC-AUC score: 65.68 |
| Positive Prediction Rate for African Americans: 0.419 | Positive Prediction Rate for African Americans: 0.434 |
| Positive Prediction Rate for Non-African Americans: 0.406 | Positive Prediction Rate for Non-African Americans: 0.5 |
| Prediction Error Rate for African Americans: 0.323 | Prediction Error Rate for African Americans: 0.34 |
| Prediction Error Rate for Non-African Americans: 0.304 | Positive Prediction Rate for Non-African Americans: 0.75 |
| False Positive Rate for African Americans: 0.3 | False Positive Rate for African Americans: 0.285 |
| False Positive Rate for Non-African Americans: 0.226 | False Positive Rate for Non-African Americans: 0.667 |
Concerns Faced in the experimentation :
- We did not experiment on the Crime and Communities due to the requirement of of ratings columns for the neighbourhoods from niche.com which is not available as a public dataset.
- We performed trial and error methods to tune parameters k( for kth quantile ) and p( for p nearest neighbours ) for maximum value of AUC score.
| Name | Roll Number |
|---|---|
| Harshvardhan Srivastava | 17EE10058 |
| Sanket Kumar Singh | 17EE30016 |
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