Ethical assessments and mitigation strategies for biases in AI-systems used during the COVID-19 pandemic

IF 6.5 1区社会学 Q1 SOCIAL SCIENCES, INTERDISCIPLINARY

Big Data & Society Pub Date : 2023-01-01 DOI:10.1177/20539517231179199

A. de Manuel, Janet Delgado, Iris Parra Jounou, T. Ausín, D. Casacuberta, Maite Cruz, Ariel Guersenzvaig, Cristian Moyano, D. Rodríguez-Arias, J. Rueda, Á. Puyol

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引用次数: 0

Abstract

The main aim of this article is to reflect on the impact of biases related to artificial intelligence (AI) systems developed to tackle issues arising from the COVID-19 pandemic, with special focus on those developed for triage and risk prediction. A secondary aim is to review assessment tools that have been developed to prevent biases in AI systems. In addition, we provide a conceptual clarification for some terms related to biases in this particular context. We focus mainly on non-racial biases that may be less considered when addressing biases in AI systems in the existing literature. In the manuscript, we found that the existence of bias in AI systems used for COVID-19 can result in algorithmic justice and that the legal frameworks and strategies developed to prevent the apparition of bias have failed to adequately consider social determinants of health. Finally, we make some recommendations on how to include more diverse professional profiles in order to develop AI systems that increase the epistemic diversity needed to tackle AI biases during the COVID-19 pandemic and beyond.

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COVID-19大流行期间使用的人工智能系统中偏见的伦理评估和缓解策略

本文的主要目的是反思与人工智能（AI）系统相关的偏见的影响，人工智能系统是为解决新冠肺炎大流行引起的问题而开发的，特别关注那些为分流和风险预测而开发的系统。第二个目的是审查为防止人工智能系统中的偏见而开发的评估工具。此外，我们还对这一特定背景下与偏见有关的一些术语进行了概念澄清。我们主要关注非种族偏见，在现有文献中解决人工智能系统中的偏见时，这些偏见可能很少被考虑。在手稿中，我们发现用于新冠肺炎的人工智能系统中存在偏见可能会导致算法公正，为防止偏见的出现而制定的法律框架和策略未能充分考虑健康的社会决定因素。最后，我们就如何包括更多样化的专业简介提出了一些建议，以开发人工智能系统，增加在新冠肺炎大流行期间及以后应对人工智能偏见所需的认识多样性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Big Data & Society SOCIAL SCIENCES, INTERDISCIPLINARY-

CiteScore

10.90

自引率

10.60%

发文量

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.