算法背景下的社会危害动态

IF 1.8 Q2 CRIMINOLOGY & PENOLOGY
H. Malik, M. Viljanen, Nea Lepinkäinen, Anne Alvesalo-Kuusi
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引用次数: 13

摘要

越来越多的证据表明,算法的可用性可以再现社会固有的偏见和歧视,增加社会经济关系中的信息不对称和权力不平衡。我们在社会介导的大规模伤害的背景下概念化这些启示。我们认为,算法技术可能不会改变危害的产生,相反,它会影响危害的质量——也就是说,危害如何、在多大程度上出现,以及危害落在谁身上。使用三个有充分记录的算法失败案例的例子,我们将关键算法研究中确定的问题与社会危害和zemology的文献相结合。将重点从社会经济结构转向社会经济技术结构,我们说明了算法技术如何通过以下方式在宏观和中观层面改变社会危害产生的动态:(1)系统化偏见和不平等;(2)以前所未有的规模加速危害传播;(3)模糊了对危害的感知。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dynamics of Social Harms in an Algorithmic Context
Growing evidence suggests that the affordances of algorithms can reproduce socially embedded bias and discrimination, increase the information asymmetry and power imbalances in socio‑economic relations. We conceptualise these affordances in the context of socially mediated mass harms. We argue that algorithmic technologies may not alter what harms arise but, instead, affect harms qualitatively—that is, how and to what extent they emerge and on whom they fall. Using the example of three well-documented cases of algorithmic failures, we integrate the concerns identified in critical algorithm studies with the literature on social harm and zemiology. Reorienting the focus from socio‑economic to socio-econo-technological structures, we illustrate how algorithmic technologies transform the dynamics of social harm production on macro and meso levels by: (1) systematising bias and inequality; (2) accelerating harm propagation on an unprecedented scale; and (3) blurring the perception of harms.  
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来源期刊
CiteScore
2.70
自引率
7.70%
发文量
50
审稿时长
9 weeks
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