How Humanity Has Always Feared Change: Are You Afraid of Artificial Intelligence?
Bibliographic record
Abstract
This article explores the relationship between fear, technological progress, and human adaptation, focusing on the rise of artificial intelligence (AI). Throughout history, significant technological advancements have provoked excitement and apprehension, from the invention of writing to the Industrial Revolution. AI is the latest episode in this constant evolution, raising fears about job automation, ethical dilemmas, loss of creativity, and existential risks. However, like past innovations, AI also presents opportunities to revolutionize healthcare, productivity, and innovation. The question guiding this analysis is How can societal fear of AI be understood not only as a reaction to disruption but also as a stimulus for ethical engagement and inclusive technological governance? The article argues that fear is not inherently negative but instead a catalyst for reflection on the ethical implications of change. Drawing on historical examples and contemporary discourse, it highlights the importance of equity, ethics, and active engagement in technological development, encouraging society to influence the trajectory of AI. The article contributes to reframing fear as a constructive force that can guide critical inquiry, promote responsible technology innovation, and foster democratic participation in determining AI futures. Finally, it suggests that how we navigate AI’s rise will assess its role in society and its potential to redefine human progress. To move forward, the article advocates for proactive strategies that center equity, interdisciplinary dialogue, and ethical foresight in designing and deploying AI systems.
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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.020 | 0.022 |
| Insufficient payload (model declined to judge) | 0.004 | 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".