Causal Learning for Socially Responsible AI

Lu Cheng · Ahmadreza Mosallanezhad · Paras Sheth · Huan Liu

15 citations0/0 files storedInternational Joint Conference on Artificial Intelligence

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Ethics and Social Impacts of AI Explainable Artificial Intelligence (XAI)Bayesian Modeling and Causal Inference

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.

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References

2018Mitigating Unwanted Biases with Adversarial Learning 2017Counterfactual Fairness 2019Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries 2019Gender Bias in Contextualized Word Embeddings 2020Gender Bias in Neural Natural Language Processing 2021Socially Responsible AI Algorithms: Issues, Purposes, and Challenges 2019Delayed Impact of Fair Machine Learning 2018Fairness in Decision-Making — The Causal Explanation Formula

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