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arXiv 2111.04439cs.CYcs.CRcs.LG
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解决来自机器学习的隐私威胁

Addressing Privacy Threats from Machine Learning

University of California San Diego(加利福尼亚大学圣迭戈分校)

Mary Anne Smart

摘要:

每年在NeurIPS上,机器学习研究人员都会聚集在一起,讨论机器学习在公共卫生、灾难应对、气候变化、教育等领域令人兴奋的应用。然而,这些研究人员中的许多人对机器学习在监视中的应用表示越来越多的关注(Nanayakkara等人,2021年)。本文简要概述了抵制这些监视技术的策略,并呼吁机器学习和人机交互研究人员之间加强合作,以应对这些技术带来的威胁。

英文摘要:

Every year at NeurIPS, machine learning researchers gather and discuss exciting applications of machine learning in areas such as public health, disaster response, climate change, education, and more. However, many of these same researchers are expressing growing concern about applications of machine learning for surveillance (Nanayakkara et al., 2021). This paper presents a brief overview of strategies for resisting these surveillance technologies and calls for greater collaboration between machine learning and human-computer interaction researchers to address the threats that these technologies pose.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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