Algorithmic self-referentiality: How machine learning pushes calculative practices to assess themselves

IF 3.6 2区 管理学 Q1 BUSINESS, FINANCE Accounting Organizations and Society Pub Date : 2024-08-14 DOI:10.1016/j.aos.2024.101567
Yuval Millo , Crawford Spence , Ruowen Xu
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Abstract

Despite the growing importance of machine learning in today's organisations, we know relatively little about how machine learning operates and how it influences calculative practices and cultures. Based on 695 hours of ethnographic fieldwork in the team of credit modellers from a large internet company in China, this study analyses the calculative culture that underpins the development of credit models. We show that credit scoring methodologies develop progressively into a self-referential set of calculative practices where substantive concerns about loan default are supplanted by more insular concerns around the seamless operation of the model. Insofar as the latter can only be measured by the model itself, this reduces the role of calculative experts to facilitators of machine learning rather than the purposeful interpreters of machine learning produced data. In this regard, credit scoring experts focus more on ensuring that models have a robust conversation with themselves rather than with managers or credit scoring agents. This matters because machine learning-driven credit scoring models end up privileging access to credit for those whose data trails more readily pass through data preparation filters rather than those who are less likely to default. We thus contribute to an understanding of how machine learning-driven calculative cultures both enact algorithmic bias and operate beyond the ken of purposeful human actors.

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算法的自我推论:机器学习如何推动计算实践自我评估
尽管机器学习在当今企业中的重要性与日俱增,但我们对机器学习如何运作以及它如何影响计算实践和文化却知之甚少。本研究基于对中国一家大型互联网公司信用建模团队长达 695 小时的人种学实地调查,分析了支撑信用模型开发的计算文化。我们发现,信用评分方法逐渐发展成为一套自我反思的计算实践,在这套实践中,对贷款违约的实质性关注被对模型无缝运行的更隐蔽的关注所取代。由于后者只能由模型本身来衡量,这就将计算专家的角色降低为机器学习的促进者,而不是机器学习所产生数据的有目的解释者。在这方面,信用评分专家更多关注的是确保模型与自身进行稳健的对话,而不是与管理者或信用评分代理进行对话。这一点很重要,因为机器学习驱动的信用评分模型最终会为那些数据轨迹更容易通过数据准备过滤器的人提供获得信贷的特权,而不是那些不太可能违约的人。因此,我们有助于理解机器学习驱动的计算文化是如何既制造算法偏见,又在有目的的人类行为者的视野之外运作的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.80
自引率
6.40%
发文量
38
期刊介绍: Accounting, Organizations & Society is a major international journal concerned with all aspects of the relationship between accounting and human behaviour, organizational structures and processes, and the changing social and political environment of the enterprise.
期刊最新文献
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