A Point and Counterpoint exchange in the Journal of Management Studies, 2026
Jie Wu · University of Aberdeen Business School
Artificial intelligence does not merely execute decisions; it distributes standing. As algorithmic systems come to mediate hiring, diagnosis, credit and legal advice, they also decide whose judgment counts, which institutions are visible, and which forms of expertise remain legible. This exchange in the Journal of Management Studies asks how such hierarchies form and whether they can be undone.
Three articles set out three positions. The Point argues that algorithmic status inequality is socio-technical: cultural assumptions embedded in system design interact with disparities in technical capability to produce self-reinforcing hierarchies, so that neither technical nor market remedies alone will dislodge them. The first Counterpoint locates the corrective in markets and organisational agency, reading the problem through the dynamic capabilities of sensing, seizing and transforming. The second Counterpoint locates the source of the problem in how AI systems are trained, and its remedy in algorithmic transparency secured through organisational governance and regulation. Read together, the three pieces map the space of available explanations and of available remedies.
All three articles are open access. The citations and DOIs are below.
Wu, J. (2026). Algorithmic status inequality: An integrative perspective on AI-driven social stratification. Journal of Management Studies.
Computational beliefs interact with computational inequalities to produce persistent status hierarchies through self-reinforcing feedback loops, illustrated in recruitment, healthcare and legal assistance.
Teece, D. J. (2026). Algorithmic status inequality through a dynamic capabilities lens: A market-based perspective. Journal of Management Studies.
Accepts the Point's diagnosis while emphasising organisational agency: the sensing, seizing and transforming capabilities through which firms can detect and reshape algorithmic status dynamics.
Triana, M. d. C., & Upadhyay, A. (2026). AI presents both problems and opportunities for minorities. Journal of Management Studies.
Argues that algorithmic status inequality is largely explained by how AI systems are trained, and, drawing on cases from financial services and human resources, that algorithmic transparency driven by organisational governance and regulatory intervention can mitigate much of it.
Citing this exchange Readers are encouraged to cite the three articles individually by their DOIs above rather than this page.