Accommodating machine learning algorithms in professional service firms

Faulconbridge, James and Sarwar, Atif and Spring, Martin (2024) Accommodating machine learning algorithms in professional service firms. Organization Studies, 45 (7). pp. 1009-1037. ISSN 0170-8406

Text (Accomodating_machine_learning_algorithms_-_Final_revision2)
Accomodating_machine_learning_algorithms_-_Final_revision2.pdf - Accepted Version
Available under License Creative Commons Attribution.
Download (486kB)

Text (Accomodating machine learning algorithms - Final published version)
Accomodating_machine_learning_algorithms_-_Final_published_version.pdf - Published Version
Available under License Creative Commons Attribution.
Download (278kB)

Abstract

Machine learning algorithms, as one form of artificial intelligence, 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 while 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 artificial intelligence in professional service firms and develops insights relevant when examining the long-term impacts of machine learning algorithms as they become ever more sophisticated.