Socially Responsible AI Algorithms: Issues, Purposes, and Challenges
Authors: Lu Cheng, Kush R. Varshney, Huan Liu
JAIR 2021 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this survey, we provide a systematic framework of Socially Responsible AI Algorithms that aims to examine the subjects of AI indifference and the need for socially responsible AI algorithms, define the objectives, and introduce the means by which we may achieve these objectives. We further discuss how to leverage this framework to improve societal well-being through protection, information, and prevention/mitigation. ... This survey complements prior work through a holistic understanding of the relations between AI systems and humans. |
| Researcher Affiliation | Collaboration | Lu Cheng EMAIL Computer Science and Engineering, Arizona State University Kush R. Varshney EMAIL IBM Research Thomas J. Watson Research Center Huan Liu EMAIL Computer Science and Engineering, Arizona State University |
| Pseudocode | No | The paper describes methods and concepts but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code for the described methodology, nor does it provide links to code repositories. |
| Open Datasets | No | The paper discusses various types of data and datasets in the context of socially responsible AI (e.g., "imbalanced data", "user behavior data"), and refers to existing studies that might use specific datasets, but it does not provide concrete access information (links, DOIs, specific repository names, or formal citations to established benchmark datasets) for publicly available or open datasets that were *used in experiments* within *this* paper itself. |
| Dataset Splits | No | The paper does not describe any experimental setup involving specific dataset splits (e.g., percentages for training, validation, or testing sets), as it is a survey and theoretical framework paper. |
| Hardware Specification | No | This paper is a survey and theoretical framework. It does not describe any experiments that would require specific hardware specifications. |
| Software Dependencies | No | This paper is a survey and theoretical framework. It does not describe any experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | This paper is a survey and theoretical framework. It does not describe any specific experimental setup, hyperparameters, or training configurations. |