A Survey on Fairness Without Demographics
Authors: Patrik Joslin Kenfack, Samira Ebrahimi Kahou, Ulrich Aïvodji
TMLR 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This survey reviews recent research efforts to enforce fairness when sensitive attributes are missing. We propose a taxonomy of existing works and, more importantly, highlight current challenges and future research directions to stimulate research in ML fairness in the setting of missing sensitive attributes. |
| Researcher Affiliation | Academia | Patrik Joslin Kenfack EMAIL ÉTS Montréal, Mila Samira Ebrahimi Kahou EMAIL University of Calgary, Mila Canada CIFAR AI Chair Ulrich Aïvodji EMAIL ÉTS Montréal, Mila |
| Pseudocode | No | The paper describes various algorithms and methods in textual format, often referring to equations or frameworks. It does not present any explicit pseudocode blocks or algorithm listings in a structured format. |
| Open Source Code | No | The paper is a survey and does not present any novel empirical methodology that would require an associated code release. It does not contain any explicit statements about releasing source code or links to a code repository for its own work. |
| Open Datasets | No | As a survey paper, this work reviews existing research and does not conduct its own empirical experiments. Therefore, it does not utilize or provide access to any specific datasets for its own methodology. |
| Dataset Splits | No | As a survey paper that does not conduct its own empirical experiments, the paper does not provide information regarding training/test/validation dataset splits. |
| Hardware Specification | No | As a survey paper that does not conduct its own empirical experiments, the paper does not specify any hardware used for running experiments. |
| Software Dependencies | No | As a survey paper that does not conduct its own empirical experiments, the paper does not list specific software dependencies with version numbers for its own methodology. |
| Experiment Setup | No | As a survey paper that does not conduct its own empirical experiments, the paper does not provide specific experimental setup details such as hyperparameter values or training configurations. |