Causal Learning for Socially Responsible AI

Causal Learning for Socially Responsible AI

Lu Cheng, Ahmadreza Mosallanezhad, Paras Sheth, Huan Liu

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
Survey Track. Pages 4374-4381. https://doi.org/10.24963/ijcai.2021/598

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.
Keywords:
Humans and AI: General
Machine learning: General
Multidisciplinary topics and applications: General
Uncertainty in AI: General