Artificial intelligence and work: transforming work, organizations, and society in an age of insecurity
Bibliographic record
Abstract
In an era marked by insecurity from neoliberalism, financial volatility, political instability, regional conflicts, pandemics, and the climate crisis, Artificial Intelligence (AI) is revolutionizing our work, organizations, societies, and the environment. This critical text explores who truly benefits from AI's development and deployment, offering a comprehensive overview of AI's nature, history, and applications. It delves into crucial themes such as the future of work, digitalization, neoliberalism's impact, power dynamics, ethics, inequality, gender, race, intersectional discrimination, and environmental sustainability. Unlike practical machine learning guides, this book examines how AI and AI-based technologies are transforming work, highlighting both benefits and potential harms. Combining critical management and leadership studies with organizational sociology, it addresses societal implications, inequality, ethics, and power often overlooked by other textbooks. John Bratton's lucid and engaging writing style brings a cutting-edge subject to life, blending breadth, critical analysis, and academic rigor. Contemporary examples illustrate AI's real-life implications for organizations and work today, while thought-provoking questions encourage readers to engage with and reflect on the topics throughout. Authored by John Bratton, an Honorary Professor at Queen’s University Belfast, and Laura Steele, a Senior Lecturer in Business and Society at Queen’s University Belfast, this interdisciplinary text is essential for students studying contemporary and emerging issues in business and management, including AI, business analytics, digitalization, and the future of work. It is also recommended for courses on the sociology of work, ethics, organization studies, management, leadership, and HRM. This book is poised to become an essential textbook for courses on AI, digitalization, and the future of work, making it a valuable resource for students and educators alike.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.051 |
| Scholarly communication | 0.021 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".