生态伦理与智慧循环经济

IF 6.5 1区 社会学 Q1 SOCIAL SCIENCES, INTERDISCIPLINARY
Rolien Hoyng
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引用次数: 1

摘要

企业关于循环经济的论述认为,在不断创新的驱动下,电子行业的增长不会危及生态的可持续性。为了实现可持续增长,其倡导者提出通过人工智能和一系列相互关联的数据中心和算法技术来优化回收。利用关键数据和算法研究、浪费理论和实证研究,本文探讨了以数据为中心和算法为中介的循环经济背景下的生态伦理。它强调了废物的不确定性和变化无常的物质性质,以及数据化和计算所固有和产生的不确定性。我的问题是:以数据为中心和算法技术的合理性、可视性和配置是如何表现和取代企业责任和透明度的概念的?为了回答这个问题,我将智能循环经济与它声称要取代的非正式回收实践进行了比较,并分析了废物与数据之间的关系以及代理的分布。具体来说,我考虑了反应能力和责任之间的过渡和滑动。从概念上讲,我将过程关系或基于内在的哲学,如柏格森和德勒兹的哲学,带入了关于废物和数据之间关系以及算法控制废物的野心的辩论中。我的目的不是要求通过控制来提高企业的责任,而是按照Amoore的云伦理来重新思考智能循环经济中的责任,从而在强化企业代理的义务论观点或新唯物主义对这一概念的谴责之外,开辟一个批判的立场。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ecological ethics and the smart circular economy
The corporate discourse on the circular economy holds that the growth of the electronics industry, driven by continuous innovation, does not imperil ecological sustainability. To achieve sustainable growth, its advocates propose optimizing recycling by means of artificial intelligence and sets of interrelated datacentric and algorithmic technologies. Drawing on critical data and algorithm studies, theories of waste, and empirical research, this paper investigates ecological ethics in the context of the datacentric and algorithmically mediated circular economy. It foregrounds the indeterminate and fickle material nature of waste as well as the uncertainties inherent in, and stemming from, datafication and computation. My question is: how do the rationalities, affordances, and dispositions of datacentric and algorithmic technologies perform and displace notions of corporate responsibility and transparency? In order to answer this question, I compare the smart circular economy to the informal recycling practices that it claims to replace, and I analyze relations between waste matter and data as well as distributions of agency. Specifically, I consider transitions and slippages between response-ability and responsibility. Conceptually, I bring process-relation or immanence-based philosophies such as Bergson's and Deleuze's into a debate about relations between waste matter and data and the ambition of algorithmic control over waste. My aim is not to demand heightened corporate responsibility enacted through control but to rethink responsibility in the smart circular economy along the lines of Amoore's cloud ethics to carve out a position of critique beyond either a deontological perspective that reinforces corporate agency or new-materialist denunciation of the concept.
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来源期刊
Big Data & Society
Big Data & Society SOCIAL SCIENCES, INTERDISCIPLINARY-
CiteScore
10.90
自引率
10.60%
发文量
59
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.
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