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cover of episode Anita Say Chan, "Predatory Data: Eugenics in Big Tech and Our Fight for an Independent Future" (U California Press, 2025)

Anita Say Chan, "Predatory Data: Eugenics in Big Tech and Our Fight for an Independent Future" (U California Press, 2025)

2025/3/25
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Michael LaMagna: 我认为大数据技术虽然带来了效率和易用性的好处，但也带来了数据收集和监控的负面影响。这些负面影响与19世纪末和20世纪初的大数据专家和算法歧视之间存在关联。 Anita Say Chan: 我对掠夺性数据的兴趣源于在信息科学学院任教的经历。我震惊地发现，学生和同事对数据科学的起源缺乏历史理解，他们不知道数据科学的基本方法——统计回归，以及大规模数据化的实践，都起源于一个多世纪前的优生学。优生学背后的动机是基于种族偏见的焦虑和对人口监控的渴望。优生学运动导致了种族隔离、阶级隔离、移民排斥法和强制绝育法等政策的出台。 我研究了1939年世界博览会上通用汽车公司委托制作的“未来世界”展品，它展示了自动化高速公路、无人驾驶汽车和规划中的郊区社区，体现了追求效率、去除低效因素的理念，这与当今科技和数据中的便利性追求存在关联。 当今的社交媒体环境体现了对人口的过度监控和实验，这与优生学运动中大规模数据收集和监控“不合格者”的实践一脉相承。优生学家对数据的量化和监控，特别是对少数族裔的监控，是他们改善社会的一种方式，他们认为民主制度和福利国家适得其反。 当今硅谷精英对“优越性”的论调，以及对自身才能的量化评估，都与优生学中关于精英群体“优越性”的论调一脉相承。20世纪初，优生学运动在美国取得了成功，通过倡导智商测试等手段，对移民进行筛选，并影响了移民法的制定。优生学运动中对数据的操纵和歪曲，以及对精英群体“优越性”的强调，与当今硅谷精英的论调存在相似之处。 为了避免掠夺性数据实践，我们需要借鉴芝加哥霍尔之家等机构的经验，发展基于正义的数据化实践，并重视AI系统中可能存在的偏见。AI系统擅长预测最可能的结局，但它不擅长处理不确定性或意外情况，这可能会导致内容生产的同质化，并限制创新和多元化。我们需要保护那些可能无法被预测的、具有变革意义的世界。

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This chapter explores the historical connection between eugenics and the current practices of big data. It reveals how many of today's data collection and analysis methods originated in the eugenic movement of the late 19th and early 20th centuries. The author highlights the lack of awareness among data scientists about this history and the ethical implications of continuing these practices.
  • Statistical regression, a fundamental method in data science, originated in eugenics.
  • Eugenics aimed to monitor and control human populations based on racial and class biases.
  • The belief that eugenics is a thing of the past is incorrect; its impact continues today.

Shownotes Transcript

Predatory Data: Eugenics in Big Tech and Our Fight for an Independent Future )(University of California Press, 2025) illuminates the throughline between the nineteenth century's anti-immigration and eugenics movements and our sprawling systems of techno-surveillance and algorithmic discrimination. With this book, Anita Say Chan offers a historical, globally multisited analysis of the relations of dispossession, misrecognition, and segregation expanded by dominant knowledge institutions in the Age of Big Data.

While technological advancement has a tendency to feel inevitable, it always has a history, including efforts to chart a path for alternative futures and the important parallel story of defiant refusal and liberatory activism. Chan explores how more than a century ago, feminist, immigrant, and other minoritized actors refused dominant institutional research norms and worked to develop alternative data practices whose methods and traditions continue to reverberate through global justice-based data initiatives today. Looking to the past to shape our future, this book charts a path for an alternative historical consciousness grounded in the pursuit of global justice.

Anita Say Chan is a feminist and decolonial scholar of Science and Technology Studies and Associate Professor of Information Sciences and Media Studies at the University of Illinois, Urbana-Champaign.

Dr. Michael LaMagna is the Information Literacy Program & Library Services Coordinator and Professor of Library Services at Delaware County Community College.

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