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Causal Learning for Socially Responsible AI

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arxiv 2104.12278 v2 pith:F7DEYB7N submitted 2021-04-25 cs.AI

classification cs.AI
keywords sraicausallearningresponsiblesociallysurveytoolsaddress
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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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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. AI4Research: A Survey of Artificial Intelligence for Scientific Research

    cs.CL 2025-07 conditional novelty 4.0 of 10

    A survey that organizes AI-for-research work into five tasks, comprehension, survey, discovery, writing, and peer review, and compiles associated tools and benchmarks.

  2. Causality-Driven Neural Network Repair: Challenges and Opportunities

    cs.LG 2025-04 conditional novelty 3.0 of 10

    A workshop position paper categorizes causality-based DNN repair methods and discusses challenges, without presenting new algorithms or experiments.

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