Rethinking Fairness: An Interdisciplinary Survey of Critiques of Hegemonic ML Fairness Approaches
Authors: Lindsay Weinberg
JAIR 2022 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This survey article assesses and compares existing critiques of current fairness-enhancing technical interventions in machine learning (ML) that draw from a range of non-computing disciplines, including philosophy, feminist studies, critical race and ethnic studies, legal studies, anthropology, and science and technology studies. |
| Researcher Affiliation | Academia | Lindsay Weinberg EMAIL Honors College, Purdue University West Lafayette, IN 47906 |
| Pseudocode | No | The paper is a survey and does not propose or describe any specific algorithms or methods in pseudocode format. It analyzes existing critiques and proposed solutions conceptually. |
| Open Source Code | No | This article is a survey of existing critiques of ML fairness approaches and does not present a new methodology that would require open-source code. It refers to open-source toolkits by third-parties, such as 'IBM s AI Fairness 360 for mitigating bias in training data (Varshney, 2018) and Google s ML-fairness-gym for anticipating the effects of automated decision systems (Srinivasan, 2020)', but does not provide code for its own content. |
| Open Datasets | No | This article is a survey and does not conduct original experiments using specific datasets. It discusses datasets and data collection practices in other research, but does not provide access information for a dataset used in its own analysis. |
| Dataset Splits | No | As a survey article, this paper does not involve experimental data processing or machine learning model training, and therefore does not specify dataset splits. |
| Hardware Specification | No | This paper is a survey article and does not describe experimental work requiring specific hardware. Therefore, no hardware specifications are provided. |
| Software Dependencies | No | As a survey article, this paper does not involve the implementation of new software or methodologies that would require specific software dependencies and version numbers. |
| Experiment Setup | No | This paper is a survey and does not describe experimental work requiring specific experimental setup details, hyperparameters, or training configurations. |