The Ethical Implications of Artificial Intelligence in the Workplace

I. Introduction

Bibliographic Information

Bankins, S., & Formosa, P. (2023). The ethical implications of artificial intelligence (AI) for meaningful work. Journal of Business Ethics, 185(4), 725-740. https://doi.org/10.1007/s10551-023-05339-7

Purpose and Thesis of the Article

The purpose of this conceptual article is to examine how the increasing use of artificial intelligence in workplaces can both enhance and diminish employees’ experiences of meaningful work. Bankins and Formosa argue that AI’s ethical implications cannot be fully understood without considering its effects on meaningful work, which they define as work perceived as worthwhile, significant, and contributing to a higher purpose. To support this argument, the authors integrate literature on meaningful work with ethical AI frameworks and propose three AI deployment pathways: replacement, “tending the machine,” and amplification, evaluated across five dimensions of meaningful work.

Thesis of this Critique

This critique argues that the article makes a strong and original conceptual contribution by integrating ethical AI principles with meaningful work theory, offering a nuanced framework for assessing AI’s workplace impacts. However, its reliance on conceptual reasoning without empirical validation, limited engagement with counterarguments, and broad generalisation across industries represent notable limitations that restrict its practical applicability.

II. Summary

The article discusses the impact of AI technology on meaningful work through changing tasks, skills, autonomy, social relationships, and perceived influence on others. The first claim by Bankins and Formosa (2023) on the ethical value of meaningful work underpinned the philosophical points of view and organisational studies. They then determine five dimensions of meaningful work, which are: task integrity, skill development and application, task significance, autonomy, and belongingness.

AI's effects on these dimensions are analysed through three implementation pathways. In the replacement pathway, AI assumes some human tasks; in tending the machine, humans manage or support AI systems; and in amplification, AI enhances human capabilities. The authors consider that these pathways may contribute to meaningful work or deny it with the requirements of AI implementation. Lastly, the ethical aspects of these effects are considered by applying the AI4People framework, which considers beneficence, non-maleficence, autonomy, justice, and explicability, and wraps that up with implications and future research.

III. Critique

Evaluation of the Introduction and Conceptual Framing

Strengths

The introduction is clear, well-motivated, and grounded in relevant literature. The authors successfully establish meaningful work as an ethically significant concern rather than merely a psychological or managerial issue. By positioning the paper at the intersection of meaningful work and ethical AI scholarship, the authors clearly articulate a research gap that justifies the study. Their definitions of AI and meaningful work are precise and supported by authoritative sources, which enhances conceptual clarity.

Weaknesses

While the introduction effectively frames the problem, it tends to assume that meaningful work is a universally shared priority among workers. Although the Bankins & Formosa briefly acknowledge trade-offs, they do not sufficiently engage with literature suggesting that workers may prioritise security, flexibility, or compensation over meaning, particularly in economically precarious contexts. This limits the inclusiveness of the ethical framing.

Evaluation of Theoretical Framework and Literature Integration

Strengths

A major strength of the article is its integration of the Job Characteristics Model with Lips-Wiersma and Morris’s humanistic framework. This synthesis produces a holistic and well-justified five-dimensional model of meaningful work. Bankins & Formosa (2023) demonstrate a strong command of interdisciplinary literature, drawing from philosophy, organisational psychology, and AI ethics. The use of the AI4People ethical framework is appropriate and well-justified, allowing the authors to systematically assess ethical implications.

Weaknesses

Despite its breadth, the literature review remains largely descriptive rather than comparative. The authors summarise prior work effectively but rarely contrast competing theories or critically evaluate disagreements within the literature. As a result, the article could more explicitly clarify how its framework improves upon or challenges existing models rather than primarily extending them.

Evaluation of Methodology and Analytical Approach

Strengths

As a conceptual paper, the article is methodologically consistent with its aims. The three AI deployment pathways provide a clear and intuitive analytical structure that helps translate abstract ethical principles into workplace-relevant scenarios. The extensive use of concrete examples strengthens the plausibility of the arguments and enhances reader engagement.

Weaknesses

The absence of empirical data is a significant limitation. While conceptual analysis is appropriate for theory-building, many claims about how workers experience AI are asserted rather than empirically demonstrated. The article also does not specify boundary conditions, such as organisational culture, national labour regulations, or industry-specific dynamics, which may significantly mediate AI’s impact on meaningful work.

Evaluation of Ethical Analysis and Argumentation

Strengths

The ethical analysis is one of the article’s strongest components. The authors convincingly show how AI can both support and undermine ethical principles depending on implementation choices. Their discussion of justice, particularly the uneven distribution of AI’s benefits and burdens across skill levels, is well-developed and ethically compelling. The emphasis on explicability and accountability is also timely and relevant, given current debates on “black box” AI systems.

Weaknesses

At times, the article adopts a normative tone that risks overstating organisational responsibility without fully addressing structural constraints, such as market competition or shareholder pressures. Additionally, while the authors acknowledge trade-offs between ethical principles, they do not offer clear guidance on how organisations should prioritise conflicting values in practice.

IV. Conclusion

Overall, Bankins and Formosa offer a careful, well-organized, and ethically deep study of the implications of AI on meaningful work. It is an important contribution of the article as it brings ethical AI discussions to the real-life workplace and provides a complex structure, which emphasizes the relevance of implementation decisions instead of regarding AI as either good or bad. This criticism anatomizes the main argument of the authors that meaningful work must be the central focus as a moral issue in the implementation of AI. Nevertheless, future studies would have the advantage of empirically testing the postulated framework on various sectors and employee groups. Comparative and longitudinal research would be beneficial in justifying the fact that the pathways and dimensions observed correctly predict the experience of workers in the long-term. Further empirical evidence can strengthen the effectiveness of this excellent conceptual background, making this article more influential in scholarly research and organisational practice in the future.