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人工智能史:剑桥科学史系的暑期学校(3月16日截止)及主题相关书目

科学史图书馆 科学史图书馆 2022-07-15

最近听说不少老师的直播课要开始了,祝大家新学期愉快。我们系这个学期正在搞一系列和AI有关的事情。下面这个研讨班系列叫Histories of AI: A Genealogy of Power,是HPS和英语系合办的,意图在一个历史的、跨学科的、去殖民化的语境里讨论AI。这个项目包括一系列读书会、暑期学校,以及可以在线参加的讨论班,希望跟进的话可以在这里subscribe他们的mailing list:https://lists.cam.ac.uk/mailman/listinfo/hps-hoai  



在项目网页上https://www.hps.cam.ac.uk/about/research-projects/histories-of-ai  还有很多背景介绍。这个研讨班主要关注AI的四个问题:AI技术的实现背后隐藏的人类劳动(Hidden Labour),AI的广泛应用对人类行为的塑造(Encoded Behavior),围绕AI的修辞(Disingenuous Rhetoric)以及AI带来的认知上的压抑、窄化与不平等(Cognitive Injustice)。


里面提到的书目我搬运整理了一下,范围很广,包括了AI、大数据、性别、政治领域的不少研究,关键很多都是17-19年新出甚至尚未出版的东西。完整书单见推送结尾。


Hidden Labour

劳动问题中的很大一部分涉及到计算,特别是早期计算机和编程发展史上的女性,是现在科技史的重点话题。在当下,人工数据录入这样的劳动不仅没有减少,反而更多了,劳动的主体往往是人力成本较低的发展中国家。996也是这个语境下的问题之一:在许多大中小城市中,我们引以为豪的“智能”便捷生活其实是依赖大量的过度劳动运转的。为了开发出方便点外卖的软件,有很多人忙到完全没时间和家人一起吃饭,这样一种智能技术下的劳动和生活状态几乎成了一种惯性。如何去反思和改善这种处境?


Gray, Mary L., and Siddharth Suri (2019). Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass. Boston: Houghton Mifflin Harcourt.
Irani, Lilly (2016). "The Hidden Faces of Automation." XRDS: Crossroads, The ACM Magazine for Students 23, no. 2: 34–37. Also see https://turkopticon.ucsd.edu/ 
Jones, Matthew L. (2016). Reckoning with Matter: Calculating Machines, Innovation, and Thinking about Thinking from Pascal to Babbage. Chicago: University of Chicago Press.
Daston, Lorraine (1994). "Enlightenment Calculations." Critical Inquiry 21, no. 1: 182–202.
Dick, Stephanie (forthcoming). Making Up Minds: Computing and Proof in the Postwar United States.
Hicks, Marie (2017). Programmed Inequality: How Britain Discarded Women Technologists and Lost Its Edge in Computing. History of Computing. Cambridge, MA: MIT Press.

Encoded Behaviour

这个主题下很多文献谈到了监视的问题。事实上,除了我们所熟知的显性的监视外,隐性的监视同样值得我们关注:当整个社会都假定你的行为是可理解的、可定义的、可程式化的,你如何保持自己作为个体的独特性?一旦你的行为被假定为可理解、可定义,这个系统就会使用这种理解来塑造你,在这个原理上,大数据对人的塑造和人对人的塑造异曲同工——最近刚读了Ian Hacking的The Looping Effects of Human Kinds,初步感觉可以连起来思考。在具体的语境中,例如工作岗位、学校等等,监视也有很多种可讨论的形式。


Lauer, Josh (2017). Creditworthy: A History of Consumer Surveillance and Financial Identity in America. Columbia Studies in the History of U.S. Capitalism. New York: Columbia University Press.
Horan, Caley Dawn (2011). "Actuarial Age: Insurance and the Emergence of Neoliberalism in the Postwar United States." Dissertation, University of Minnesota.
Igo, Sarah E. (2018). "Josh Lauer. Creditworthy: A History of Consumer Surveillance and Financial Identity in America." The American Historical Review 123, no. 2: 605–7.
Rosenthal, Caitlin (2018). Accounting for Slavery: Masters and Management. Cambridge, Massachusetts: Harvard University Press.
Levy, Karen Elinor Conway (2014). “The Automation of Compliance: Techno-Legal Regulation in the United States Trucking Industry." Princeton University.
Dinnen, Zara (2017). The Digital Banal: New Media and American Literature and Culture. New York: Columbia University Press.
Dick, Stephanie (forthcoming). Making Up Minds: Computing and Proof in the Postwar United States.

Disingenuous Rhetoric

人类对人工智能的想象和修辞在历史上一直是一个重要话题。最近重读Frankenstein时看到一篇文章用anxiety of creation这个词来分析这本书,即人类对创造一个和自己一样的智能生命的焦虑。现在很常见的情况是,对人工智能的想象经常让我们人格化甚至妖魔化这项技术,进入一种模式化的想象中,而忽视了真正的技术现实。我们关于人工智能的修辞的背后是很多政治和经济因素,下面导论里提到的Sophia就是一个很好的例子。在这个问题下,我想我们思考现有修辞的同时,还可以思考如何在艺术、文学和实践中创造新的修辞。

下面还放了一个Leverhulme的讲座,从17世纪至今的知识史视角讲AI,同样也涉及到修辞的问题。


Fast, E. and Horvitz, E. (2017). "Long-Term Trends in the Public Perception of Artificial Intelligence." AAAI (February): 963–969.
Ching-Hua Chuan, Wan-Hsiu Tsai and Su Yeon Cho (2019). "Framing Artificial Intelligence in American Newspapers." AIES 2019.
Reccia, Gabriel (2020). "The Governance of AI," in AI Narratives: A History of Imaginative Thinking about Intelligent Machines, ed with Stephen Cave and Kanta Dihal (Oxford: Oxford University Press).
Taylor, Astra (2018). "The Automation Charade." Logic Magazine, August.
Schaffer, Simon (1996). Babbage's Calculating Engines and the Factory System. Réseaux. The French journal of communication, volume 4, n°2, 1996. pp. 271-298.

Cognitive Injustice

认知问题与其说是AI的应用带来的,不如说它根植于整个数码时代的认知方式。认知工具例如搜索引擎、图像识别等等技术的发展,让我们把越来越多的认知官能让渡给了技术。其结果是人类自己反而成了被剥夺知识和判断的一方,知识不再被人类拥有,而是被特定的媒介和算法拥有,被掌握技术的群体使用,这加剧了认知的不对称性。AI是这种情形的集中表现:当你将知识和判断都完全让渡给智能,你就会发现你也让渡了一部分价值。例如导论里提到的案例:谁判断你的性别?少数群体往往最先承担这种认知让渡带来的价值问题。


Ricaurte, Paola (2019). "Data Epistemologies, The Coloniality of Power, and Resistance." Television & New Media, March 7.
Couldry, Nick, and Ulises A. Mejias (2018). "Data Colonialism: Rethinking Big Data's Relation to the Contemporary Subject." Television & New Media.
Yale, Elizabeth (2015). "The History of Archives: The State of the Discipline." Book History 18, no. 1: 332–59.
Keyes, Os (2018). "The Misgendering Machines: Trans/HCI Implications of Automatic Gender Recognition." Proceedings of the ACM on Human-Computer Interaction 2, no. CSCW (November): 1–22.
Mahendran, D.D. (2011) "Race and computation: an existential phenomenological inquiry concerning man, mind, and the body." Unpublished PhD thesis. University of California.


现在暑期学校已经开放报名了,日期是7月12-18日,有差旅补助可以申请。各个领域的研究者,包括艺术家、媒介学者、社会学者等等都是欢迎的,我估计社会运动家更受欢迎,哈哈。主题并不会限于上面的四个问题。报名地址https://www.hps.cam.ac.uk/about/research-projects/histories-of-ai/activities/summer-school 


再提一下Leverhulme Centre for the Future of Intelligence (CFI) ,是base在剑桥的人工智能研究中心,由Leverhulme Trust资助,和我们系的合作很多。http://lcfi.ac.uk/  他们三月份还有一个AI in the History of Knowledge的讲座,这里贴一下摘要。


完整参考文献列表

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