Blind Justice: Fairness with Encrypted Sensitive Attributes
Authors: Niki Kilbertus, Adria Gascon, Matt Kusner, Michael Veale, Krishna Gummadi, Adrian Weller
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we show how to overcome both doubts and that fair training, certiļ¬cation and veriļ¬cation are feasible for realistic datasets. 5.1. Experimental Setup and Datasets We work with two separate code bases. Our Python code... The full MPC protocol is implemented in C++... We consider 5 real world datasets, namely the adult (Adult), German credit (German), and bank market (Bank) datasets from the UCI machine learning repository (Lichman, 2013), the stop, question and frisk 2012 dataset (SQF),3 and the COMPAS dataset (Angwin et al., 2016) (COMPAS). For practical purposes (see Section 4), we subsample 2i examples from each dataset with the largest possible i, see Table 1. Moreover, we also run on synthetic data... |
| Researcher Affiliation | Academia | Niki Kilbertus 1 2 Adri a Gasc on 3 4 Matt Kusner 3 4 Michael Veale 5 Krishna P. Gummadi 6 Adrian Weller 2 3 1Max Planck Institute for Intelligent Systems 2University of Cambridge 3The Alan Turing Institute 4University of Warwick 5University College London 6Max Planck Institute for Software Systems. |
| Pseudocode | Yes | Algorithm 1 in Section B in the appendix describes the computations M and REG have to run for fair model training using the Lagrangian multiplier technique and the p%-rule from eq. (9). |
| Open Source Code | Yes | Code is available at https://github.com/nikikilbertus/blind-justice |
| Open Datasets | Yes | We consider 5 real world datasets, namely the adult (Adult), German credit (German), and bank market (Bank) datasets from the UCI machine learning repository (Lichman, 2013), the stop, question and frisk 2012 dataset (SQF),3 and the COMPAS dataset (Angwin et al., 2016) (COMPAS). |
| Dataset Splits | No | The paper mentions subsampling data and evaluating 'test set accuracy' but does not provide specific details on the dataset splits (e.g., percentages for train/validation/test, specific sample counts, or the methodology used for splitting). |
| Hardware Specification | No | The paper mentions that experiments were run 'on a laptop computer' in Section 5.3, but no specific hardware details such as CPU, GPU models, or memory specifications are provided. |
| Software Dependencies | No | The paper states that the MPC protocol is implemented in 'C++ on top of the Obliv-C garbled circuits framework (Zahur & Evans, 2015a) and the Absentminded Crypto Kit (lib)', but it does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | Table 1 lists 'training over 10 epochs with batch size 64'. Section 5.2 mentions running methods 'for a range of constraint values in [10 4, 100]'. |