Comments (4)
The sample datasets can be used for implementing this.
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I'm unclear on what the output should be.
For example, Dwork et al. focus on using the Lipshitz condition as a constraint on the optimization function. We want to take the predictions from an unconstrained algorithm and determine how far from the LIpshitz condition it is.
As a first step, would we want two matrices one with distance measured in the feature space and a second with distance measured in the outcome space (from which we could determine a score such as what fraction of the pairs of points fail the conditions)?
Similarly, would the input be the whole feature space for a prediction? This seems necessary, but undesirable, since we would require all the data.
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@saleiro I've outlined how I think this should work in the issue ticket. Does that outline of functions seem reasonable? I'll start with the clustering?
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@anisfeld I suggest that you create a README file within the individual module and outline exactly what you are going to implement. Let's abstract if the features are passed in the df or not. It's up to the final user to decide what she wants to use as representation. They can even pass different dfs based on different representations, train/test splits over time etc...
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Related Issues (20)
- Add method: Fair Label Noise Correction
- Add Method: Group Thresholding
- Add method: Fairness Through Unawareness
- Update GitHub pages HOT 1
- Unit and Integration Testing HOT 1
- `Audit` class improvements
- Method in `Experiment` to plot the results
- ValueError: Replace has to be set to True when upsampling the population frac > 1 HOT 1
- Better handling of experiment output folders
- Sensitive attribute type check is erroneously expecting object type
- Install fails on python 3.12 HOT 2
- Move included datasets to other place HOT 1
- Enhance Robustness by Handling Missing Values and Group-wise Calculation of PPR
- Enhance summary visual to use more than color
- Update Pass / Fail Labels
- Inconsistent column name for target variable HOT 1
- Attribute Error ap.disparity
- Add type checking at major parts of the code
- Integration with some ML platforms (such as ML Flow or weights and biases)
- Add a readme or page on the existing metrics of fairness HOT 2
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