Aug 2026· International Journal Administration, Business & Organization· 0 citations· 24 references
TL;DR
This study investigates the efficiency paradox of AI, emphasizing the impact of technical optimization on human values, social cohesiveness, and workplace culture and indicates that the ethical and human-centric integration of AI is crucial to ensure that efficiency gains align with inclusivity, justice, and the maintenance of social well-being in contemporary workplaces.
Abstract
Artificial Intelligence (AI) significantly transforms contemporary workplaces by improving efficiency and production. Nonetheless, its implementation also incurs unforeseen social and ethical ramifications. This study investigates the efficiency paradox of AI, emphasizing the impact of technical optimization on human values, social cohesiveness, and workplace culture. A qualitative literature assessment of studies from 2020 to 2025 delineates four principal themes: efficiency and automation, algorithmic bias, alteration of professional identity, and social interaction. Research indicates that AI can lead to dehumanization, deskilling, and diminished interpersonal communication, whereas algorithmic governance may exacerbate bias and inequality within businesses. These factors undermine trust and communal involvement. The study indicates that the ethical and human-centric integration of AI is crucial to ensure that efficiency gains align with inclusivity, justice, and the maintenance of social well-being in contemporary workplaces.
The rapid integration of artificial intelligence (AI) into organizational contexts is reshaping how employees work, interact, and respond to changing workplace dynamics. Although AI is often expected to improve efficiency and reduce workload, emerging evidence points to a potential dark side, as AI adoption may intensify competitive pressure within organizations. Motivated by this paradox, this study examines how AI reliance may erode workplace social capital by fostering involution—an excessive and inefficient form of competition characterized by escalating effort and diminishing returns. This research focuses on digitally enabled and highly competitive workplace settings, where employee performance and outputs are more visible, transparent, and comparable. We propose that, from employees’ perspective, AI reliance is associated with higher levels of involution through elevated performance expectations and anxiety. Using survey data from employees in China, the study hypotheses were tested using structural equation modeling. The results support the proposed model and indicate a significant sequential mediation pattern linking AI reliance, performance expectations, employee anxiety, and workplace involution. Notably, the direct relationship between AI reliance and involution is not significant, suggesting that AI reliance is associated with employees’ defensive competitive behavior primarily through employees’ perception of external evaluative pressures and internal psychological responses. These findings highlight the unintended social consequences of AI adoption in organizations, and underscore the importance of protecting employee wellbeing, communicating realistic performance expectations, and maintaining workplace social capital when implementing AI in digitally enabled organizations.
Ruochen Huang· Frontiers in Psychology· 0 citations
Preliminary empirical support for ethical vigilance as a relevant construct in AI governance and organizational decision-making is provided and useful insights are provided into how AI systems can be designed to be explainable, accountable and ethically sound to counter rising organizational risks.
Avinash Chopra· VINE Journal of Information...· 0 citations
This study examines the transformative impact of artificial intelligence (AI) on the future of work from a human-centered, interdisciplinary perspective. Drawing on insights from sociology, ethics, education and organizational behavior studies, it highlights how AI reshapes job roles, skills, workplace dynamics and labor identities. The paper emphasizes that AI is not a neutral or inevitable force but a socio-technical system shaped by power, culture and policy. Key challenges include algorithmic management, digital exclusion, emotional labor and a lack of ethical opacity. To address these issues, the study advocates for policies that prioritize transparency, inclusivity and human agency through educational reform, robust labor protections and ethical AI governance. Furthermore, the paper underscores that social scientists should not remain mere observers of these processes but can act as active agents in shaping a fair and equitable integration of AI into work life. Developed within a conceptual framework, this study offers guidance for future research and policymaking, highlighting the necessity of interdisciplinary collaboration to ensure that AI not only enhances efficiency but also promotes justice, well-being, and sustainability.
Cumali Kılıç· Çukurova Üniversitesi Sosyal...· 0 citations
The adoption of generative artificial intelligence among communication practitioners and researchers surged after the launch of ChatGPT in November 2022, urging practitioners to critically engage in exploring pathways for fostering socially responsible and environmentally sustainable AI practices.
Artificial intelligence (AI) is increasingly permeating social work, attracting both enthusiasm for its potential benefits and concern regarding its ethical, relational, and professional implications. Whilst prevalent discourse has often positioned social workers along a binary spectrum of either optimism or scepticism, such framing risks oversimplifying the complex sociotechnical dynamics at play. This article argues that social work requires a more nuanced, philosophically informed engagement with AI, one that recognises technology as neither neutral nor inevitable, but as socially shaped, value-laden, and co-constitutive of practice. Drawing on perspectives from the philosophy of technology, the paper examines how AI mediates professional judgement, decision-making, and the relational foundations of practice via a narrative review of social work literature. It interrogates the claim that AI can depoliticise or objectify social work by obscuring the normative choices embedded within technology design and deployment. Rather than adopting a deterministic stance, the article advocates for a critical, reflexive approach that centres professional values, care ethics, and the lived experience of service users. Opportunities for AI to enhance practice, such as through administrative support, predictive insights, and improved access to services, are considered alongside challenges related to power, accountability, data ethics, and the risk of amplifying social inequalities. The paper concludes by outlining implications for education, research, and practice, emphasising the need for social workers to actively shape the development and governance of AI in ways that uphold social justice, human dignity, and the core purpose of the profession.
The rapid expansion of AI-mediated workplaces has transformed digital work culture, reshaping how organizations manage, monitor, and evaluate employees. While AI-driven systems enhance operational efficiency and support data-driven governance, they also raise ethical concerns regarding surveillance, autonomy, fairness, and employee well-being. This study presents a Systematic Literature Review (SLR) using the PRISMA framework and thematic synthesis to identify and analyze 44 peer-reviewed articles published between 2015 and 2026 from six databases: Scopus, Web of Science, ScienceDirect, Emerald Insight, SpringerLink, and Google Scholar. The findings are organized into six themes: AI adoption in digital workplaces, employee trust in AI, ethical tensions in algorithmic decision- making, algorithmic management and workplace control, psychological well-being, and human-centered AI governance. The review shows that, despite improving organizational performance, AI-mediated systems contribute to technostress, burnout, AI anxiety, identity threats, and reduced worker autonomy. Key ethical concerns include algorithmic bias, opacity, discrimination, and inadequate institutional governance. This study contributes an integrative conceptual framework linking algorithmic workplace practices, ethical challenges, and employee well-being, moderated by organizational support, AI transparency, digital capability, and ethical leadership, alongside a practical framework for implementing sustainable, human-centered AI governance in digital workplaces.
Yulianto, Udin Saryono· Digital Theory, Culture &...· 0 citations