An Employee-Centric Perspective on AI at Work: Insights for Optimizing Implementation and Use
Bibliographic record
Abstract
The developments in Artificial Intelligence (AI) skyrocket and organizations are increasingly implementing AI systems in their work processes. As a consequence, AI systems seem to dictate how organizations and work are transformed. Our view is that such a technocratic perspective can harm both employees and organizations, and therefore argue that research needs to adopt an employee-centric perspective on the implementation and use of AI at work. The symposium explores how individual employees and teams react to, interact and collaborate with AI. Our first presentation offers a detailed insight into how the implementation of an AI-based decision support systems changes jobs. The second presenter will present a cognitive perspective on employee adaptation to advanced technology and highlight the role of individual differences. The third presenter focuses on the importance of employee readiness for working with embodied AI systems and how training can enhance readiness. The fourth presentation discusses the challenges and opportunities when onboarding an autonomous AI agent into a all human team. Finally, our fifth presenter explores whether employees assign gender-based stereotypes to AI systems and how these perceptions affect the integration of AI in the workplace. Following the presentations, we will hold an open discussion led by the chairs, to discuss the implications of the presented studies for research and the implications for organizational practice. How the introduction of AI re-shapes work: An example from the railway sector Author: Lena Schneider; Author: Daniel Boos; - The Role of Cognition in the Adaptation to Technologies: An updated taxonomy Author: Eva Gößwein; University of Duisburg-Essen Author: Magnus Liebherr; University of Duisburg-Essen Understanding and Enhancing Employee Readiness for Human-Robot Collaboration:A mixed-method approach Author: Raquel Salcedo Gil; Eindhoven University of Technology Author: Sonja Rispens; Author: Pascale Le Blanc; Eindhoven University of Technology Author: Anna-Sophie Ulfert; Welcome to the Team? How to Onboard Autonomous Agents to All-Human Teams Author: Thomas Alexander O'Neill; University of Calgary Author: Jonn Henke; University of Calgary Author: Aimee Kane; Duquesne University Author: Christopher Flathmann; Clemson University Author: Nathan McNeese; Clemson University The Impact of AI Gender Features on Human-AI Interaction Author: Huiru Evangeline Yang; IESEG School of Management Author: Yekaterina Bezrukova; University at Buffalo, School of Management Author: Terri L Griffith; Simon Fraser University Author: Chester S. Spell; Rutgers University
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.026 | 0.018 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".