How Algorithmic Literacy and Demographic Background Shape Public Attitudes on AI
Bibliographic record
Abstract
The rapid advancement of Artificial Intelligence (AI) technologies has driven significant global investments and numerous government-led initiatives aimed at enhancing public awareness and knowledge of AI. Countries like Canada are at the forefront of these efforts, seeking to foster widespread adoption by improving algorithmic literacy. However, the relationship between algorithmic literacy, Internet usage, and societal attitudes toward algorithms remains insufficiently understood. This thesis investigates how socioeconomic background shapes Internet usage, how Internet usage influences algorithmic literacy, and how these factors collectively impact attitudes toward algorithms. Using data from an online survey of N = 2,025 participants across Canada, the findings reveal that younger and more educated individuals engage more frequently online, resulting in higher levels of algorithmic literacy. Paradoxically, increased algorithmic literacy is associated with more critical attitudes toward algorithms, reflecting heightened awareness of their limitations and biases. Furthermore, the analysis demonstrates that demographic factors, particularly age and education, indirectly influence attitudes toward algorithms through mediated pathways involving Internet usage and algorithmic literacy. These results highlight the complex interplay between literacy and trust, suggesting that improving public attitudes toward algorithms requires not only literacy initiatives but also systemic interventions that address issues such as transparency and fairness. This research provides insights for policymakers, educators, and researchers seeking to foster equitable and informed public engagement with AI technologies while addressing their societal implications
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".