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..
Data-Driven Methods for Balancing Fairness and Efficiency in Ride-Pooling
Authors: Naveen Raman, Sanket Shah, John Dickerson
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To evaluate our methods, we utilize the New York City taxi data set [New York City, 2016]... We run experiments comparing the different objective functions on both profitability and fairness metrics. |
| Researcher Affiliation | Academia | Naveen Raman1 , Sanket Shah2 , John P. Dickerson1 1University of Maryland 2Harvard University EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes algorithmic steps but does not include structured pseudocode or an algorithm block. |
| Open Source Code | Yes | 1Our code and data is publicly available at https://github.com/naveenr414/ijcai-rideshare |
| Open Datasets | Yes | To evaluate our methods, we utilize the New York City taxi data set [New York City, 2016] |
| Dataset Splits | No | The paper mentions training and testing but does not explicitly provide specific train/validation/test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., CPU, GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper indicates the use of deep learning and a neural network but does not provide specific software names with version numbers for reproducibility (e.g., Python 3.x, TensorFlow 2.x, PyTorch 1.x). |
| Experiment Setup | No | The paper has a section titled 'Experiment Setup' (4.4), but it defers the concrete hyperparameter values and detailed value function training specifics to 'Appendix B', which is not part of the main text provided. |