Accommodating machine learning algorithms in professional service firms

IF 4.9 1区管理学 Q1 MANAGEMENT

Organization Studies Pub Date : 2024-04-27 DOI:10.1177/01708406241252930

James R Faulconbridge, Atif Sarwar, Martin Spring

{"title":"Accommodating machine learning algorithms in professional service firms","authors":"James R Faulconbridge, Atif Sarwar, Martin Spring","doi":"10.1177/01708406241252930","DOIUrl":null,"url":null,"abstract":"Machine learning algorithms, as one form of artificial intelligence (AI), are significant for professional work because they create the possibility for some predictions, interpretations and judgements that inform decision making to be made by algorithms. However, little is known about whether it is possible to transform professional work to incorporate machine learning whilst also addressing negative responses from professionals whose work is changed by inscrutable algorithms. Through original empirical analysis of the effects of machine learning algorithms on the work of accountants and lawyers, this paper identifies the role of accommodating machine learning algorithms in professional service firms. Accommodating machine learning algorithms involves strategic responses that both justify adoption in the context of the possibilities and new contributions of machine learning algorithms and respond to the algorithms’ limitations and opaque and inscrutable nature. The analysis advances understanding of the processes that enable or inhibit the cooperative adoption of AI in PSFs and develops insights relevant when examining the long-term impacts of machine learning algorithms as they become ever more sophisticated.","PeriodicalId":48423,"journal":{"name":"Organization Studies","volume":"16 1","pages":""},"PeriodicalIF":4.9000,"publicationDate":"2024-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Organization Studies","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1177/01708406241252930","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MANAGEMENT","Score":null,"Total":0}

引用次数: 0

Abstract

Machine learning algorithms, as one form of artificial intelligence (AI), are significant for professional work because they create the possibility for some predictions, interpretations and judgements that inform decision making to be made by algorithms. However, little is known about whether it is possible to transform professional work to incorporate machine learning whilst also addressing negative responses from professionals whose work is changed by inscrutable algorithms. Through original empirical analysis of the effects of machine learning algorithms on the work of accountants and lawyers, this paper identifies the role of accommodating machine learning algorithms in professional service firms. Accommodating machine learning algorithms involves strategic responses that both justify adoption in the context of the possibilities and new contributions of machine learning algorithms and respond to the algorithms’ limitations and opaque and inscrutable nature. The analysis advances understanding of the processes that enable or inhibit the cooperative adoption of AI in PSFs and develops insights relevant when examining the long-term impacts of machine learning algorithms as they become ever more sophisticated.

查看原文本刊更多论文

专业服务公司适应机器学习算法

机器学习算法作为人工智能（AI）的一种形式，对专业工作具有重要意义，因为它们为算法做出一些预测、解释和判断提供了可能性，而这些预测、解释和判断又为决策提供了依据。然而，人们对以下问题知之甚少：是否有可能将专业工作转化为机器学习，同时解决专业人员因不可捉摸的算法改变工作而产生的负面反应。本文通过对机器学习算法对会计师和律师工作影响的原创性实证分析，确定了适应机器学习算法在专业服务公司中的作用。适应机器学习算法涉及战略应对措施，既要在机器学习算法的可能性和新贡献的背景下证明采用机器学习算法的合理性，又要应对算法的局限性以及不透明和不可捉摸的性质。该分析加深了人们对促进或抑制在 PSF 中合作采用人工智能的过程的理解，并在研究机器学习算法日益复杂的长期影响时提出了相关见解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Organization Studies MANAGEMENT-

CiteScore

11.50

自引率

16.70%

发文量

期刊介绍： Organisation Studies (OS) aims to promote the understanding of organizations, organizing and the organized, and the social relevance of that understanding. It encourages the interplay between theorizing and empirical research, in the belief that they should be mutually informative. It is a multidisciplinary peer-reviewed journal which is open to contributions of high quality, from any perspective relevant to the field and from any country. Organization Studies is, in particular, a supranational journal which gives special attention to national and cultural similarities and differences worldwide. This is reflected by its international editorial board and publisher and its collaboration with EGOS, the European Group for Organizational Studies. OS publishes papers that fully or partly draw on empirical data to make their contribution to organization theory and practice. Thus, OS welcomes work that in any form draws on empirical work to make strong theoretical and empirical contributions. If your paper is not drawing on empirical data in any form, we advise you to submit your work to Organization Theory – another journal under the auspices of the European Group for Organizational Studies (EGOS) – instead.