Algorithms as co‐workers: Human algorithm role interactions in algorithmic work

TLDR

Algorithmic work assigns operational and managerial tasks to algorithms, but human–algorithm interactions suffer from absent dialogue, opaque output generation, and difficulty overriding directives, creating unclear roles for workers. The article investigates how humans and algorithms interact as co‑workers in algorithmic work. Using an organisational‑role framework, the study models algorithms as role senders and humans as role takers, drawing on interviews with 15 Uber drivers, 1,700 forum posts, and Uber’s web pages to examine algorithm‑driven taxi driving. The analysis shows algorithms act as multi‑role senders with entangled roles, while humans face role conflict and ambiguity, and the algorithm’s recording of actions without capturing cognitive reactions leads to broken‑loop learning, with implications for IS scholarship, practice, and policy.

Abstract

Abstract In algorithmic work, algorithms execute operational and management tasks such as work allocation, task tracking and performance evaluation. Humans and algorithms interact with one another to accomplish work so that the algorithm takes on the role of a co‐worker. Human–algorithm interactions are characterised by problematic issues such as absence of mutually co‐constructed dialogue, lack of transparency regarding how algorithmic outputs are generated, and difficulty of over‐riding algorithmic directive – conditions that create lack of clarity for the human worker. This article examines human–algorithm role interactions in algorithmic work. Drawing on the theoretical framing of organisational roles, we theorise on the algorithm as role sender and the human as the role taker. We explain how the algorithm is a multi‐role sender with entangled roles, while the human as role taker experiences algorithm‐driven role conflict and role ambiguity. Further, while the algorithm records all of the human's task actions, it is ignorant of the human's cognitive reactions – it undergoes what we conceptualise as ‘broken loop learning’. The empirical context of our study is algorithm‐driven taxi driving (in the United States) exemplified by companies such as Uber. We draw from data that include interviews with 15 Uber drivers, a netnographic study of 1700 discussion threads among Uber drivers from two popular online forums, and analysis of Uber's web pages. Implications for IS scholarship, practice and policy are discussed.