This article investigates the complex nexus of capitalism, racial oppression,
and artificial intelligence (AI), revealing how these elements coalesce to
deepen social inequities. By tracing the historical exploitation of
marginalized communities through capitalist practices, the study demonstrates
how AI technologies not only reflect but also amplify societal biases,
particularly in exacerbating racial disparities. Through a focused analysis,
the paper presents how AI's development and application exploit marginalized
groups via mechanisms such as gig economy labor abuses, biased facial
recognition technologies, and the disproportionate mental health burdens placed
on these communities. These examples underscore the critical role of AI in
reinforcing and intensifying existing inequalities. Concluding that unregulated
AI significantly threatens to compound current oppressions, the article calls
for a concerted effort towards responsible AI development. This entails
adopting a holistic approach that rectifies systemic flaws and champions the
empowerment of marginalized individuals, ensuring that technological
advancement contributes to societal healing rather than perpetuating cycles of
exploitation.

本研究调查了资本主义、种族压迫和人工智能之间复杂的联系，揭示了这些元素如何共同作用来加深社会不平等。通过追溯资本主义实践对边缘化社区的历史剥削，本研究表明人工智能技术不仅反映了社会偏见，而且放大了这种偏见，特别是在加剧种族差异方面。通过针对性的分析，本文介绍了人工智能发展和应用如何通过诸如零工经济的劳工滥用、有偏见的面部识别技术以及给这些社区带来不成比例的心理健康负担等机制来剥削边缘化群体。这些例子突显了人工智能在加强和加剧现有不平等方面的关键作用。文章得出的结论是，不受监管的人工智能严重威胁到现有压迫的累加，呼吁共同努力实现负责任的人工智能发展。这需要采取一种整体性的方法，修正系统缺陷，倡导边缘化个体的赋权，确保技术进步有助于社会的愈合，而不是延续剥削循环。

剥削边缘：资本主义如何以压迫少数群体为代价推动人工智能

Exploiting the Margin: How Capitalism Fuels AI at the Expense of  Minoritized Groups

Artificial intelligence (AI) systems have the potential to revolutionize
clinical practices, including improving diagnostic accuracy and surgical
decision-making, while also reducing costs and manpower. However, it is
important to recognize that these systems may perpetuate social inequities or
demonstrate biases, such as those based on race or gender. Such biases can
occur before, during, or after the development of AI models, making it critical
to understand and address potential biases to enable the accurate and reliable
application of AI models in clinical settings. To mitigate bias concerns during
model development, we surveyed recent publications on different debiasing
methods in the fields of biomedical natural language processing (NLP) or
computer vision (CV). Then we discussed the methods that have been applied in
the biomedical domain to address bias. We performed our literature search on
PubMed, ACM digital library, and IEEE Xplore of relevant articles published
between January 2018 and December 2023 using multiple combinations of keywords.
We then filtered the result of 10,041 articles automatically with loose
constraints, and manually inspected the abstracts of the remaining 890 articles
to identify the 55 articles included in this review. Additional articles in the
references are also included in this review. We discuss each method and compare
its strengths and weaknesses. Finally, we review other potential methods from
the general domain that could be applied to biomedicine to address bias and
improve fairness.The bias of AIs in biomedicine can originate from multiple
sources. Existing debiasing methods that focus on algorithms can be categorized
into distributional or algorithmic.

人工智能系统在临床实践中有潜力革新，包括提高诊断准确性和手术决策，并降低成本和人力。然而，需要认识到这些系统可能会延续社会不公平或表现出以种族或性别为基础的偏见。为了准确可靠地应用人工智能模型于临床环境中，理解和解决潜在的偏见至关重要。通过调查生物医学自然语言处理或计算机视觉领域的最新出版物，我们对生物医学领域中应用的偏见处理方法进行了概述，并讨论了每种方法的优势和弱点。最后，我们还回顾了从一般领域中可能应用于生物医学领域以解决偏见和提高公平性的其他潜在方法。

应对生物医学中人工智能公平性和偏见的最新方法调查

A survey of recent methods for addressing AI fairness and bias in  biomedicine

The potential for machine learning (ML) systems to amplify social inequities
and unfairness is receiving increasing popular and academic attention. A surge
of recent work has focused on the development of algorithmic tools to assess
and mitigate such unfairness. If these tools are to have a positive impact on
industry practice, however, it is crucial that their design be informed by an
understanding of real-world needs. Through 35 semi-structured interviews and an
anonymous survey of 267 ML practitioners, we conduct the first systematic
investigation of commercial product teams' challenges and needs for support in
developing fairer ML systems. We identify areas of alignment and disconnect
between the challenges faced by industry practitioners and solutions proposed
in the fair ML research literature. Based on these findings, we highlight
directions for future ML and HCI research that will better address industry
practitioners' needs.

本文通过 35 个半结构化面谈和 267 个机器学习从业者的匿名调查，系统研究了商业产品团队开发公平的机器学习系统所面临的挑战和需求，发现了业界从业者面临的挑战及公平机器学习的研究文献中提出的解决方案之间的一些不协调之处，并提出了未来机器学习与人机交互研究的方向，以更好地满足产业从业者的需求。