mlsanityData to 7 Oct 2026

Causal Learning for Socially Responsible AI

arXiv 2104.12278 ↗First posted 25 Apr 2021cs.AIcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 39 of its 68 references that are in the map. First posted on arXiv 25 Apr 2021.

Builds on

39 of 68 references.

References · 39

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27 more
  1. 2019Gender Bias in Contextualized Word Embeddingscited by 203
  2. 2018Delayed Impact of Fair Machine Learningcited by 183
  3. 2016Unbiased Learning-to-Rank with Biased Feedbackcited by 155
  4. 2019Mitigate Bias in Face Recognition using Skewness-Aware Reinforcement Learningcited by 145
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  10. 2019Neural Network Attributions: A Causal Perspectivecited by 77
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Cited by year

Citing papers per year: 2022: 2; 2023: 3; 2024: 0; 2025: 2.

7 papers.

  1. 2022Evaluation Methods and Measures for Causal Learning Algorithmscited by 23
  2. 2023Counterfactual Learning on Graphs: A Surveycited by 22
  3. 2022Counterfactual Neural Temporal Point Process for Estimating Causal Influence of Misinformation on Social Mediacited by 20
  4. 2023From Google Gemini to OpenAI Q* (Q-Star): A Survey of Reshaping the Generative Artificial Intelligence (AI) Research Landscapecited by 18
  5. 2025Recent Advances in Malware Detection: Graph Learning and Explainabilitycited by 6
  6. 2023When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlookcited by 4
  7. 2025Causality-Driven Neural Network Repair: Challenges and Opportunitiescited by 0