Citizenship, Censorship, and Democracy in the Age of Artificial Intelligence
Bibliographic record
Abstract
Abstract This chapter delves into the ethical dilemmas that arise from the incorporation of artificial intelligence (AI) into the field of education. It emphasises the importance of media literacy, AI literacy, and critical use of digital technologies in order to combat information conflicts, political manipulation, and AI inequality, among other issues. Potential threats to citizenship, such as AI censorship and disinformation, are examined in this chapter. Discourse is devoted to the dangers of deepfake technology as it pertains to the dissemination of false information and the manipulation of public sentiment; the significance of comprehending AI fundamentals and enforcing ethical standards is underscored. Notwithstanding the potential hazards, this chapter acknowledges the prospective advantages of AI in the field of education, which encompass gamification and adaptive learning paths. The text culminates by emphasising the significance of AI acculturation in enabling individuals to comprehend the ethical intricacies and arrive at well-informed judgements regarding the impact of AI on democracy, education, and citizenship.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".