Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Fairness and Bias in Online Selection
Authors: Jose Correa, Andres Cristi, Paul Duetting, Ashkan Norouzi-Fard
ICML 2021 | Venue PDF | 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 in๏ฌuence 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. |