Sampling Ex-Post Group-Fair Rankings
Authors: Sruthi Gorantla, Amit Deshpande, Anand Louis
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We give empirical evidence that our algorithms compare favorably against recent baselines for fairness and ranking utility on real-world data sets. |
| Researcher Affiliation | Collaboration | 1Indian Institute of Science, Bengaluru 2Microsoft Research, Bengaluru |
| Pseudocode | Yes | Algorithm 1 Sampling a uniform random group-fair representation; Algorithm 2 Sampling an approximately uniform random group-fair representation |
| Open Source Code | Yes | Implementation of our algorithms and the baselines has been made available for reproducibility github.com/sruthigorantla/Sampling Ex Post Group Fair Rankings. |
| Open Datasets | Yes | We evaluate our results on the German Credit Risk dataset comprising credit risk scoring of 1000 adult German residents [Dua and Graff, 2017]... We also evaluate our algorithm on the IIT-JEE 2009 dataset, also used in Celis et al. (2020b). |
| Dataset Splits | No | The paper does not specify training, validation, or test dataset splits in the context of splitting data for model training/evaluation, as their algorithms are sampling-based and operate on existing in-group rankings rather than trained models. |
| Hardware Specification | Yes | The experiments were run on a Quad-Core Intel Core i5 processor consisting of 4 cores, with a clock speed of 2.3 GHz and DRAM of 8GB. |
| Software Dependencies | No | The paper mentions using 'Polytope Sampler' and 'Matlab' (in a footnote) but does not provide specific version numbers for these software components used in the experiments. |
| Experiment Setup | Yes | We use k = 100 and b = 50 in the experiments. We sample 1000 rankings for randomized algorithms and output the mean and standard deviation. |