Abstract
AbstractThe ongoing development and adoption of digital technologies such as AI in business brings ethical concerns and challenges. Main topics are the design of digital technologies, their tasks, and competencies in organizational practice, and their collaboration with humans. Previous guidelines on digital ethics mainly consider technological aspects such as the nondiscriminatory design of AI, its transparency, and technically constrained (distributed) agency as priorities in AI systems, leaving the consideration of the human factor and the implementation of ethical guidelines in organizational practice unclear. We analyze the relationship between human–computer interaction (HCI), autonomy, and worker involvement with its impact on the experience of alienation at work for workers. We argue that the consideration of autonomy and worker involvement is crucial for HCI. Based on a quantitative empirical study of 1989 workers in Germany, the analysis shows that when worker involvement is high, the effect of HCI use on alienation decreases. The study results contribute to the understanding of the use of digital technologies with regard to worker involvement, reveal a blind spot in widespread ethical debates about AI, and have practical implications with regard to digital ethics in organizational practice.
Funder
Ministry of Economy, Labor and Tourism Baden-Württemberg
Universität Hohenheim
Publisher
Springer Science and Business Media LLC
Subject
General Earth and Planetary Sciences
Reference109 articles.
1. Brynjolfsson, E., McAfee, A.: The second machine age: work, progress, and prosperity in a time of brilliant technologies. WW Norton & Company, New York (2014). https://doi.org/10.1414/84259
2. Susskind, R.E., Susskind, D.: The future of the professions: how technology will transform the work of human experts. Oxford University Press, Oxford (2015). https://doi.org/10.1093/oso/9780198713395.001.0001
3. European Commission. Ethics guidelines for trustworthy ai. https://op.europa.eu/en/publication-detail/-/publication/d3988569-0434-11ea-8c1f-01aa75ed71a1 (2019). Accessed 08 June 2021
4. Balasubramanian, N., Ye, Y., Xu, M.: Substituting human decision-making with machine learning: implications for organizational learning. Acad. Manag. Rev. 47, 448–465 (2022). https://doi.org/10.5465/amr.2019.0470
5. Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J.-F., Breazeal, C., Crandall, J.W., Christakis, N.A., Couzin, I.D., Jackson, M.O.: Machine behaviour. Nature 568, 477–486 (2019)