The Initial Stage of the Artificial Intelligence Revolution: Access to Basic Income is a Human Rights Issue
Bibliographic record
Abstract
The Initial Stage of the Artificial Intelligence Revolution:Access to Basic Income is a Human Rights Issue Ehsan Jozaghi To the Editor, Background In addition to Ontario and Manitoba, universal basic income pilot programs have been run in Africa, Asia, Europe, and South America.1 The programs have been a resounding success, giving participants dignity and improving their health.2 For example, there has been a decreased use of alcohol and tobacco and improved sleep and mental health among recipients.1 At the same time, family members reported improvements in their children's school performance, health, nutrition, stability, and social networks.1,3 Unfortunately, despite such successes, the pilot programs have not materialized into permanent national initiatives.4 This is particularly important in the era where many nations face a growing housing shortage, poverty, and inequality. In addition to the increasing inequality of income and housing shortage, there has also been a shift from traditional economic approaches to increasingly knowledge-based economies in which access to affordable post-secondary education, vocations/trades, life-long learning, and online modes of learning play a crucial factor in securing higher wages.5 Artificial intelligence revolution The shift to a knowledge-based economy has been linked to the initial stage of the artificial intelligence revolution, which is changing society, economy, culture, science, and medicine much more quickly than the first or second industrial revolutions.6 While previous work has attributed the rapid nature of change during industrial revolutions to many positive developments (e.g., new medicine and scientific discoveries), there have also been some inadvertently adverse effects.6 Similarly, the initial stage of the AI revolution has helped in numerous positive ways, such as developing new innovative methods to quickly develop a vaccine during the Covid-19 pandemic, which saved millions of lives and contributed trillions of dollars to the global economy by enabling faster economic recovery.7 Lamentably, the growing AI advancement and technologies have begun an irreversible reality that AI will replace countless human tasks/jobs.6 Therefore, it is expected that without universal basic income support, millions of people will become homeless and suffer severe health outcomes due to AI's advancements. Conclusion As AI's evolutionary process enters its early stage, rapid change is expected to shock the economy, society, health, and social safety net without appropriate [End Page xv] government interventions.6 Universal basic income support is an innovative solution to tackle this inevitable reality while allowing citizens to upgrade their educational qualifications via government subsidies and social programs. Therefore, universal basic income will become a human rights issue in the AI era when AI takes over many tasks that were previously performed by millions of citizens. Governmental economic policies and inaction will continue to affect the social determinants of health directly. How governments decide to implement basic income support can influence health and stability across the country for future generations. Please address all correspondence to: Ehsan Jozaghi, Faculty of Dentistry, University of British Columbia, 2206 East Mall, Vancouver, BC Canada V6T 1Z3. Reference 1. Basic Income Earth Network. Countries that have tried universal basic income. Toronto, ON: Basic Income Earth Network, 2024. Available at https://basicincomecanada.org/countries-that-have-tried-universal-basic-income/. Google Scholar 2. McDowell T, Ferdosi M. The experiences of social assistance recipients on the Ontario basic income pilot. Can Rev Sociol. 2020 Nov;57(4):681–707. Epub 2020 Nov 5. https://doi.org/10.1111/cars.12306 PMid:33151642 Google Scholar 3. Hamilton L, Mulvale JP. "Human again": The (unrealized) promise of basic income in Ontario. J Poverty. 2019;23(7):576–99. https://doi.org/10.1080/10875549.2019.1616242 Google Scholar 4. Law S. As Ontario faces a certified class action, former recipients of basic income pilot share their struggles. Toronto, ON: CBC News, 2024. Available at https://www.cbc.ca/news/canada/thunder-bay/ontario-basic-income-pilot-class-action-1.7149814. 5. Jozaghi E. A new innovative method to measure the demographic representation of scientists via Google Scholar. Method Innov. 2019 Sept-Dec;12(3):2059799119884273. https://doi.org/10.1177/2059799119884273 Google Scholar 6. Jozaghi E, Jozaghi P. A new innovative method for evaluating monarchies (crowns): A...
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.018 | 0.030 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".