On Fairness in Decision-Making under Uncertainty: Definitions, Computation, and Comparison
Authors: Chongjie Zhang, Julie Shah
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Motivated by different application domains, we propose four maximin fairness criteria and develop corresponding algorithms for computing their optimal policies. Furthermore, we analyze the connections between these criteria and discuss and compare their characteristics. and Table 1: Comparison of different fairness criteria Criterion Horizon Algorithm Complexity Optimal policy Granularity |
| Researcher Affiliation | Academia | Chongjie Zhang and Julie A. Shah Computer Science and Artiļ¬cial Intelligence Laboratory Massachusetts Institute of Technology Cambridge, MA 02139 {chongjie,julie a shah}@csail.mit.edu |
| Pseudocode | No | The paper describes algorithms using numbered steps within paragraphs, but it does not include formal pseudocode blocks or figures explicitly labeled as 'Algorithm'. |
| Open Source Code | No | The paper does not contain any statement or link providing access to open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not perform experiments on specific datasets, thus no information on public dataset availability is provided. |
| Dataset Splits | No | The paper is theoretical and does not report on experimental validation using datasets, so no dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe computational experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper mentions general tools like 'existing LP solvers' or 'value iteration, linear programming' but does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe experimental procedures, therefore no specific experimental setup details like hyperparameters or training configurations are provided. |