Deep Neural Networks (DNNs) have been the driving force behind many of the recent advances in machine learning. However, research has shown that DNNs are vulnerable to adversarial examples -- input samples that have been perturbed to force DNN-based models to make errors. As a result, Adversarial Machine Learning (AdvML) has gained a lot of attention, and researchers have investigated these vulnerabilities in various settings and modalities. In addition, DNNs have also been found to incorporate embedded bias and often produce unexplainable predictions, which can result in anti-social AI applications. The emergence of new AI technologies that leverage Large Language Models (LLMs), such as ChatGPT and GPT-4, increases the risk of producing anti-social applications at scale. AdvML for Social Good (AdvML4G) is an emerging field that repurposes the AdvML bug to invent pro-social applications. Regulators, practitioners, and researchers should collaborate to encourage the development of pro-social applications and hinder the development of anti-social ones. In this work, we provide the first comprehensive review of the emerging field of AdvML4G. This paper encompasses a taxonomy that highlights the emergence of AdvML4G, a discussion of the differences and similarities between AdvML4G and AdvML, a taxonomy covering social good-related concepts and aspects, an exploration of the motivations behind the emergence of AdvML4G at the intersection of ML4G and AdvML, and an extensive summary of the works that utilize AdvML4G as an auxiliary tool for innovating pro-social applications. Finally, we elaborate upon various challenges and open research issues that require significant attention from the research community.

翻译：深度神经网络（DNN）是机器学习领域诸多最新进展的核心驱动力。然而，研究表明，DNN容易受到对抗性样本的攻击——这些输入样本经过精心扰动，迫使基于DNN的模型产生错误。因此，对抗性机器学习（AdvML）受到广泛关注，研究者们已在多种场景和模态中探究了这些脆弱性。此外，DNN还被发现存在嵌入偏见，并常产生难以解释的预测，这可能导致反社会的人工智能应用。诸如ChatGPT和GPT-4等利用大型语言模型（LLM）的新兴人工智能技术，进一步加剧了规模化生成反社会应用的风险。面向社会福祉的对抗性机器学习（AdvML4G）是一个新兴领域，旨在将AdvML的缺陷转化为创新社会福祉应用的契机。监管者、从业者和研究者应协同合作，推动社会福祉类应用的发展，并遏制反社会类应用的扩张。本文首次对新近兴起的AdvML4G领域进行了全面综述，涵盖以下内容：突出AdvML4G涌现的分类体系；探讨AdvML4G与AdvML的异同；覆盖社会福祉相关概念与方面的分类框架；剖析ML4G与AdvML交叉领域催生AdvML4G的动机；以及系统总结利用AdvML4G作为辅助工具创新社会福祉应用的研究工作。最后，我们详细阐述了亟待研究界关注的各种挑战和开放性问题。