Fairness and Bias in Online Selection
Authors: Jose Correa, Andres Cristi, Paul Duetting, Ashkan Norouzi-Fard
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we empirically validate our results on synthetical and real-world experiments1. We present experiments for the multi-color secretary problem in Section 4.1 and the multi-color prophet problem in Section 4.2. |
| Researcher Affiliation | Collaboration | 1Department of Industrial Engineering, Universidad de Chile, Santiago, Chile. 2Google Research, Z urich, Switzerland. |
| Pseudocode | Yes | Algorithm 1 GROUPTHRESHOLDS(t), Algorithm 2 FAIR GENERAL PROPHET, Algorithm 3 FAIR IID PROPHET |
| Open Source Code | Yes | An implementation of these experiments is available at https://github.com/google-research/google-research/tree/master/fairness_and_bias_in_online_selection. |
| Open Datasets | Yes | We consider a dataset containing one record for each phone call by a Portuguese banking institution (Moro et al., 2014). We consider a dataset containing the influence of the users of the Pokec social network (Takac & Z abovsk y, 2012). |
| Dataset Splits | No | The paper describes its experiments on datasets but does not explicitly state train/validation/test splits with percentages or sample counts, or reference predefined splits for reproduction. |
| Hardware Specification | No | The paper does not mention any specific hardware (GPU, CPU, cloud instance type) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper describes the number of runs for experiments and data distributions for synthetic datasets, but does not provide specific hyperparameters, training configurations, or system-level settings for reproducibility. |