Governing by algorithms: studies on the ethical implications of artificial intelligence (AI) in platform work

Abstract

Artificial intelligence (AI) is an emerging technology that can mimic human cognitive functions and gradually learn from new inputs. While academic works have demonstrated the contribution of this technology to improving operational efficiency in various fields, such as business and the public sector, its ethical implications for humans and society are still under debate because the distinctive characteristics of AI, including its ability to perform cognitive functions, self-learning capabilities, and continuous data ingestion, enable AI causing, exaggerating, or alleviating ethical problems. In addition to academic attention to the ethical implications of AI for humans and society, policymakers focus on regulating the development and use of AI in various fields. However, these regulations remain conceptual and are based primarily on the opinions of governmental and industry elites. Given the rapid development of AI and the limitations of regulations in accounting for individuals’ experiences, academic research on how AI affects individuals’ lives remains critical.

Among the diverse contexts of implementing AI, algorithmic management on digital labour platforms – using data and learning algorithms to coordinate and control platform workers’ activities – is particularly important. One reason is the increasing proportion of platform workers in the global workforce. The other reason is the dominant role of AI algorithms in managerial decision-making on digital labour platforms compared with traditional organisations where human managers retain their power in final decision-making. Therefore, whether algorithmic management is designed and developed in an ethical manner shapes platform workers’ everyday lives. This thesis aims to concentrate on platform workers’ experiences to advance the current understanding of the ethical implications of algorithmic management on platform workers.

In pursuit of this aim, Chapter 1 outlines the research questions central to this thesis, presents the structure of this thesis, and explains the philosophical position with which this thesis echoes. This introduction chapter reflects on the rationale behind the formulation of the research questions and the decisions on the research design. Chapter 2 presents the literature review of this thesis, including an overall review of the literature on AI and ethics, as well as a narrower, organising review of 95 articles that aims to understand how empirical studies on AI’s ethical implications incorporate ethics and offer viable research opportunities for future research. This chapter also explains how these research opportunities illuminate studies 1 and 2 of this thesis.

Chapter 3 presents the first study of this thesis that quantitatively synthesises the most examined relationships in studies on how algorithmic management influences workers. This quantitative synthesis employs a meta-analysis approach, involving 134 correlations gathered from 30 empirical studies that include 35,737 research participants. The insightful findings from this synthesis enable this thesis to identify the forms of algorithmic management, i.e., regulatory and guiding algorithmic control, the critical consequences of implementing algorithmic management on workers, and the well-examined mediating effects. More importantly, this synthesis highlights the dominant ethical perspectives, namely the principle-based ethical perspectives, that have been employed in examining the ethical implications of algorithmic management for workers. These findings, along with those in Chapter 2, are discussed to support the detailed explanation of this thesis’s problem statement, as outlined in Chapter 4.

Given the limitations of allowing principle-based ethical perspectives to dominate existing research on the ethical implications of algorithmic management for platform workers, Chapter 5 presents the second study of this thesis, which seeks to problematise the underlying assumptions and challenge this prevailing dominance. The second study begins by introducing the underlying assumptions, critiques of these assumptions, and how a new ethical perspective, namely the ethics of care, can address these critiques. The second study subsequently employs an exploratory qualitative approach, involving 1,194 minutes of interview data and other types of qualitative data, to understand the obstacles to enacting ethics of care under algorithmic management, as well as how platform workers respond to a lack of care. This study presents rich findings on how platform organisations can enhance the design and implementation of algorithmic management to support platform workers and foster caring relationships among platform participants.

In Chapter 6, this thesis concludes with a summary of the contents of all chapters and a detailed discussion of the implications of two studies for future research and real-world practices. Additionally, Chapter 6 presents another discussion on the limitations of the two studies and corresponding future research opportunities.

Date of Award	15 Jan 2026
Original language	English
Awarding Institution	University of Nottingham
Supervisor	David Phang (Supervisor) & Alain Chong (Supervisor)