Causal Learning for Socially Responsible AI
Open accessAbstract
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.
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Meta-learning focuses on few-shot generalization, enabling AI to handle a wide range of tasks with minimal data, underlining its importance in developing versatile and adaptable AI systems.
“It focuses on few-shot generalization, enabling AI to handle a wide range of tasks with minimal data, underlining its importance in developing versatile and adaptable AI systems [255], [256], [254], [257].”
From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [255]
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Cited by
Sorted by citations
McIntosh, T. R., Susnjak, T., Liu, T., Watters, P., Xu, D., Liu, D., & Halgamuge, M. N. (2025) · Technologies · doi:10.3390/technologies13020051 · Cited by 102 · see citing works
“A significant challenge in this area is dealing with intersectional biases [221], [222], [223] and understanding the causal interactions that may contribute to these biases [224], [225], [226], [227].”
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- Authors
- Cheng, L., Mosallanezhad, A., Sheth, P., & Liu, H.
- Affiliations
- Arizona State University (US)
- Venue
- Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
- Published
- 2021-08
- Pages
- 4374-4381
- OpenAlex
- W3190201191
- Cited by
- 15
- References
- 51
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 4 · 2023: 2 · 2022: 3
- FWCI
- 2.03
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Joint Conference on Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- National Science Foundation (1909555)
- Army Research Office (W911NF2020124, W911NF2110030)
- Army Research Laboratory (W911NF2020124, W911NF2110030)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Keywords
- causal learning, AI ethics, fairness, causal inference, algorithmic fairness
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