From Micro to Macro: Diverse Stakeholder Responses to AI in Workplaces
Bibliographic record
Abstract
Artificial intelligence (AI) is transforming our experiences at work, with diverse stakeholders, such as employees, managers, job applicants, designers, and consumers, exhibiting a wide range of responses to its use. Understanding these reactions is critical for leveraging AI’s potential and mitigating unintended consequences. This symposium offers a comprehensive exploration of stakeholder perspectives on workplace AI, integrating diverse theoretical frameworks, methodological approaches, and levels of analysis. Our symposium spans micro, meso, and macro levels to address the complexity of AI’s impact on organizations. One research team adopts a micro-level lens, using quantitative methods to analyze employees’ responses to AI, focusing on issues such as job insecurity and professional identity uncertainty. The second team introduces the construct of Everyday Algorithmic Resistance to capture the various subtle forms of resistance workers can exhibit against algorithmic management. Another team presents a conceptual paper exploring the job applicants’ perspective, with a particular emphasis on signals of exclusion in AI-driven recruitment. At the meso level, a group of researchers combines qualitative and quantitative methods to examine how managers and employees collaboratively navigate the integration of AI in organizations. Finally, a macro-level perspective is provided through an integrative review, which considers the broader societal implications of AI, focusing on its effects on manufacturers, users, and systemic structures. This symposium integrates multiple stakeholder perspectives and methodological approaches to address the multifaceted nature of AI’s influence on the workplace. It offers theoretical advancements and practical guidance for researchers and practitioners committed to navigating the challenges and opportunities AI presents. Employee Reactions to Hiring Algorithms Author: Mehnaz Rafi; University of Calgary Author: Justin Weinhardt; University of Calgary Everyday Algorithmic Resistance: Initial Concept and Empirical Investigation Author: Novika Grasiaswaty Kamal; University of Glasgow Author: Belgin Okay-Somerville; University of Glasgow Author: Adina Dudau; Signals of Exclusion: AI-Driven Recruitment Amidst DEI Divestment in a Changing Political Landscape Author: Devalina Nag; University of San Diego Author: Farhana Nusrat; University of San Diego AI Monitoring on the Frontlines: A Longitudinal Study on the Mediating Role of Managers Author: Gabrielle Voiseux; The University of British Columbia Author: Sima Sajjadiani; Author: Danielle Van Jaarsveld; The University of British Columbia Author: David Douglas Walker; The University of British Columbia AI and Responsible Management: Exploring Technology, Human Interaction, and Sustainable Development Author: Farley S. Nobre; Federal University of Paraná Author: Ana Cristina O. Siqueira; William Paterson University of New Jersey
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".