临床研究的社交媒体广告:网络定向的伦理和数据保护意义

IF 6.5 1区 社会学 Q1 SOCIAL SCIENCES, INTERDISCIPLINARY
Rainer Mühlhoff, Theresa Willem
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引用次数: 2

摘要

社交媒体广告通过提供数据驱动的目标定位方法,彻底改变了广告界。社交媒体广告刚刚站稳脚跟的一个领域是招募临床研究参与者。在这里,和其他地方一样,社交媒体广告承诺每花一分钱就能获得更高的收益,因为这项技术可以更好地接触到高度专业化的群体。在这篇文章中,我们指出了社交媒体上临床研究广告带来的严重社会风险。我们发现,用于临床研究的社交媒体广告在许多情况下侵犯了个人用户的隐私(R1),通过帮助平台公司训练可应用于所有用户的医疗信息预测模型来制造集体隐私风险(R2),利用了(生物医学)研究伦理(R3)中现有指南的弱点,并对(生物医学)的研究质量有害(R4)。我们认为,从平衡的角度来看,善意的承诺是站不住脚的,这些承诺通常与临床研究中使用社交媒体广告有关。因此,我们呼吁更新研究伦理准则,更好地监管大数据和推理分析。我们得出的结论是,只要数据分析和人工智能公司对社交媒体使用数据的处理(甚至是匿名的)以及预测模型的训练没有得到充分的监管,社交媒体广告——尤其是针对弱势患者群体——就不适合作为临床研究的招募工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Social media advertising for clinical studies: Ethical and data protection implications of online targeting
Social media advertising has revolutionised the advertising world by providing data-driven targeting methods. One area where social media advertising is just gaining a foothold is in the recruitment of clinical study participants. Here, as everywhere, social media advertising promises more yield per money spent because the technology can better reach highly specialised groups. In this article, we point out severe societal risks posed by advertising for clinical studies on social media. We show that social media advertising for clinical studies in many cases violates the privacy of individual users (R1), creates collective privacy risks by helping platform companies train predictive models of medical information that can be applied to all their users (R2), exploits the weaknesses of existing guidelines in (biomedical) research ethics (R3) and is detrimental to the quality of (biomedical) research (R4). We argue that the well-intentioned promises, which are often associated with the use of social media advertising for clinical studies, are untenable from a balanced point of view. Consequently, we call for updates of research ethics guidelines and better regulation of Big Data and inferential analytics. We conclude that social media advertising – especially with vulnerable patient populations – is not suitable as a recruitment tool for clinical studies as long as the processing of (even anonymised) social media usage data and the training of predictive models by data analytics and artificial intelligence companies is not sufficiently regulated.
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来源期刊
Big Data & Society
Big Data & Society SOCIAL SCIENCES, INTERDISCIPLINARY-
CiteScore
10.90
自引率
10.60%
发文量
59
审稿时长
11 weeks
期刊介绍: Big Data & Society (BD&S) is an open access, peer-reviewed scholarly journal that publishes interdisciplinary work principally in the social sciences, humanities, and computing and their intersections with the arts and natural sciences. The journal focuses on the implications of Big Data for societies and aims to connect debates about Big Data practices and their effects on various sectors such as academia, social life, industry, business, and government. BD&S considers Big Data as an emerging field of practices, not solely defined by but generative of unique data qualities such as high volume, granularity, data linking, and mining. The journal pays attention to digital content generated both online and offline, encompassing social media, search engines, closed networks (e.g., commercial or government transactions), and open networks like digital archives, open government, and crowdsourced data. Rather than providing a fixed definition of Big Data, BD&S encourages interdisciplinary inquiries, debates, and studies on various topics and themes related to Big Data practices. BD&S seeks contributions that analyze Big Data practices, involve empirical engagements and experiments with innovative methods, and reflect on the consequences of these practices for the representation, realization, and governance of societies. As a digital-only journal, BD&S's platform can accommodate multimedia formats such as complex images, dynamic visualizations, videos, and audio content. The contents of the journal encompass peer-reviewed research articles, colloquia, bookcasts, think pieces, state-of-the-art methods, and work by early career researchers.
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