Causal Learning for Socially Responsible AI

Lu Cheng; Ahmadreza Mosallanezhad; Paras Sheth; Huan Liu

IJCAI 2021poster24 citations

Causal Learning for Socially Responsible AI

Lu Cheng, Ahmadreza Mosallanezhad, Paras Sheth, Huan Liu

Abstract

There have been increasing concerns about Artificial Intelligence (AI) due to its unfathomable potential power. To make AI address ethical challenges and shun undesirable outcomes, researchers proposed to develop socially responsible AI (SRAI). One of these approaches is causal learning (CL). We survey state-of-the-art methods of CL for SRAI. We begin by examining the seven CL tools to enhance the social responsibility of AI, then review how existing works have succeeded using these tools to tackle issues in developing SRAI such as fairness. The goal of this survey is to bring forefront the potentials and promises of CL for SRAI.

Humans and AI: GeneralMachine learning: GeneralMultidisciplinary topics and applications: GeneralUncertainty in AI: General

BibTeX

@inproceedings{ijcai2021p598,
  title     = {Causal Learning for Socially Responsible AI},
  author    = {Cheng, Lu and Mosallanezhad, Ahmadreza and Sheth, Paras and Liu, Huan},
  booktitle = {Proceedings of the Thirtieth International Joint Conference on
               Artificial Intelligence, {IJCAI-21}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Zhi-Hua Zhou},
  pages     = {4374--4381},
  year      = {2021},
  month     = {8},
  note      = {Survey Track},
  doi       = {10.24963/ijcai.2021/598},
  url       = {https://doi.org/10.24963/ijcai.2021/598},
}