Beyond efficiency: epistemic well-being at work under algorithmic governance

Abstract Workplace well-being research has generated extensive evidence on subjective experience and on organisational antecedents, such as leadership, climate, justice, and work design. Yet, these conditions are usually theorised as predictors of affective, evaluative, or functioning outcomes rather than as constitutive arrangements that determine whether workers can exercise professional judgement with practical consequence. In parallel, debates on artificial intelligence (AI) often frame intelligent technologies as instruments of efficiency, optimisation, or control. Focusing on systems whose outputs enter consequential organisational evaluation and decision-making (including algorithmic management, predictive analytics, automated decision-making, decision-support systems, workplace surveillance, and generative AI) this article asks whether organisational arrangements preserve or erode the capacity for judgement, contestation, and epistemic responsibility. It introduces Epistemic Well-being at Work (EWW) as an organisational condition in which individuals and collectives can exercise responsible judgement within socio-technical structures of authority. Drawing on the view of intelligent technologies as emergent epistemic regimes, this paper develops the Human Sustainability and Epistemic Well-being Framework (HSEW-F), linking AI-mediated governance, human dignity at work, and sustainable organisational well-being. The contribution is conceptual: it extends, rather than displaces, established well-being approaches by making the effective exercise of epistemic agency visible as a structural condition of human sustainability.

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Publication Details

Journal
AI & Society
Published
2026-09-05
DOI
https://doi.org/10.1007/s00146-026-03351-9
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
0.00

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article

Beyond efficiency: epistemic well-being at work under algorithmic governance

Mario Alberto Salazar-Altamirano, Rafael Ravina-Ripoll
AI & Society
Ethics and Social Impacts of AI
article

Beyond efficiency: epistemic well-being at work under algorithmic governance

Mario Alberto Salazar-Altamirano, Rafael Ravina-Ripoll
article en

Abstract

Abstract Workplace well-being research has generated extensive evidence on subjective experience and on organisational antecedents, such as leadership, climate, justice, and work design. Yet, these conditions are usually theorised as predictors of affective, evaluative, or functioning outcomes rather than as constitutive arrangements that determine whether workers can exercise professional judgement with practical consequence. In parallel, debates on artificial intelligence (AI) often frame intelligent technologies as instruments of efficiency, optimisation, or control. Focusing on systems whose outputs enter consequential organisational evaluation and decision-making (including algorithmic management, predictive analytics, automated decision-making, decision-support systems, workplace surveillance, and generative AI) this article asks whether organisational arrangements preserve or erode the capacity for judgement, contestation, and epistemic responsibility. It introduces Epistemic Well-being at Work (EWW) as an organisational condition in which individuals and collectives can exercise responsible judgement within socio-technical structures of authority. Drawing on the view of intelligent technologies as emergent epistemic regimes, this paper develops the Human Sustainability and Epistemic Well-being Framework (HSEW-F), linking AI-mediated governance, human dignity at work, and sustainable organisational well-being. The contribution is conceptual: it extends, rather than displaces, established well-being approaches by making the effective exercise of epistemic agency visible as a structural condition of human sustainability.

AI & Society
Universidad de Cádiz (ES), CETYS Universidad (MX), Autonomous University of Tamaulipas (MX)
Universidad de Cádiz
Openalex Percentile: Top 7%
Ethics and Social Impacts of AI
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