Doctoral Dissertation Research: Recovering the Polyvalent Genealogies of Machine Learning, 1948 - 2017

机器学习技术目前在刑事司法、信用风险、社会福利、招聘和国会选区重划等不同领域做出“高风险”判断。这些技术使用从历史社会数据中学习的模式来做出这些决定。强调预测而不是数据集创建的环境导致机器学习系统优先针对弱势群体进行非常不利的社会判断，同时使那些受到这些决定的人更难以抗议不公平待遇。这项研究通过追踪技术和非技术人员（包括资助机构）如何历史地理解机器学习系统可以和应该实现的目标，来探索这种机器学习系统的局限性。特别注意的是，在20世纪世纪的不同时刻，研究人员重视的“学习”的形式，以及理论概念的出现，这些概念受到现有物质设备能力的限制，甚至被定义。这项工作使以前在人工智能和机器学习历史中被忽略的女性和男性的努力变得可见，并开发了一种定量方法来记录学科的创新如何取决于跨学科和跨学科的研究网络。最后，本项目追溯了资源分配给特定研究团体如何刺激了物理和社会科学中相邻和看似无关的学术研究领域的科学创新。从这个意义上说，机器学习学科为不同子领域的思想传播建模提供了一个有用的案例研究，采用了定性和定量的历史研究。首先，我们仔细阅读了九个大学和政府档案，以重建机器学习和人工智能研究人员可用的机构组织、人际研究网络和材料计算设备。其次，使用主题建模和词嵌入的新组合对1950年至2017年数百万篇计算机协会全文文章的语料库进行调查，以追踪话语影响力如何在不同的子学科中传播。产生了四种研究产品：（1）详细介绍用于分析文章语料库的新颖方法的技术机器学习出版物，（2）追溯机器学习早期历史的科学史文章，（3）讨论当代机器学习研究的政策和伦理含义的普通观众“思考片”，（4）并公开发布项目代码及后续统计生成的文章语料。在本研究期间发现的重要档案记录的数字副本将通过哥伦比亚大学的数字存储库免费提供，只要这是可能的版权和访问限制持有机构。收集的档案材料和对文章语料库的计算研究将用于共同PI的博士论文，探索机器学习如何被用于对个人进行分类，想象社区，以及立法形式的社会和政治证据。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

Machine learning techniques currently make "high-stakes" judgments in areas as diverse as criminal justice, credit risk, social welfare, hiring, and congressional redistricting. Such techniques make these decisions using patterns learned from historical social data. Emphasis on prediction rather than the circumstances of dataset creation have led to machine learning systems that preferentially target vulnerable populations for disparately adverse social judgments while making it more difficult for those subject to these decisions to protest unfair treatment. This study explores the limitations of such machine learning systems by tracing how technical and non-technical people, including funding agencies, have historically understood what machine learning systems could and should achieve. Particular care is given to the forms of "learning" valued by researchers during different moments in the 20th century, and to the emergence of theoretical concepts that were constrained and even defined by the capabilities of the available material devices. This work makes visible the efforts of women and men previously omitted in histories of artificial intelligence and machine learning, and develops a quantitative method to document how the innovations of a discipline are contingent upon interdisciplinary and transdisciplinary research networks. Finally, this project traces how the allocation of resources to particular research communities spurred scientific innovation in adjacent and seemingly unrelated academic research fields in the physical and social sciences. In this sense, the discipline of machine learning provides a useful case study for modeling the propagation of ideas across different subfields.Both qualitative and quantitative historical research is employed. First, nine university and government archives are perused to reconstruct the institutional organizations, interpersonal research networks, and material computing devices available to machine learning and artificial intelligence researchers. Second, an investigation is conducted using a novel combination of topic modeling and word embeddings on a corpus of millions of full-text Association of Computing Machinery articles from 1950 to 2017 to trace how discursive influence propagates across disparate sub-disciplines. Four research products are generated: (1) a technical machine learning publication detailing the novel method used to analyze the article corpus, (2) a history of science article tracing the early history of machine learning, (3) a general audience "think piece" discussing the policy and ethical implications of contemporary machine learning research, (4) and the public release of the project code and the subsequent statistics generated from the article corpus. Digital copies of salient archive records discovered during this research study will be made freely available via Columbia University's Digital Repository, insofar as this is possible given the copyright and access restrictions of holding institutions. Archive materials collected and computational study of the article corpus will be used in the co-PI's doctoral dissertation exploring how machine learning has been used to classify individuals, imagine communities, and legislate forms of social and political evidence.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.