Вплив поширення штучного інтелекту на реалізацію права на достатній рівень життя
Bibliographic record
Abstract
This article is devoted to a comprehensive understanding of the impact of artificial intelligence technology diffusion on the transmission channels of productivity into household income and, as a result, the possibility of realising the human right to an adequate standard of living. The aim of the study is to examine the issues involved in developing an analytical framework that integrates the definitional and legal foundations for regulating artificial intelligence, specific empirical indicators of labour market tension, and political and economic pathways for the socially just conversion of technological dividends into improved working conditions and wages. The methodology combines a doctrinal analysis of legal definitions, a professionally specific approach, as well as a taxonomy of sectoral AI intensity (Organisation for Economic Co-operation and Development) and triangulation of official statistical data from several technologically developed regions (US Bureau of Labour Statistics, Eurostat, Statistics Canada) with industry reviews (Organisation for Economic Co-operation and Development, McKinsey). The study concluded that the post-pandemic “cooling” of job vacancies in the US, EU and Canada is largely cyclical. At the same time, a specific signal of artificial intelligence is manifested in the internal recomposition of demand – routine tasks are being replaced by positions with high human-machine complementarity. At the same time, a specific signal from artificial intelligence is manifested in the internal recomposition of demand – routine tasks are being replaced by positions with high human-machine complementarity; absolute automation puts pressure on the labour share, but regenerative applications of artificial intelligence have the potential to generate a double social dividend with complementary investments in training and staff skills; The legitimacy of using artificial intelligence in high-risk and high-responsibility areas (medicine, justice, defence) directly depends on the explainability of artificial intelligence, reliable accountability, and the implementation of ‘human in the loop’ standards. The scientific novelty lies in identifying the advantages of a risk-based regulatory model (a permissive corridor with operational criteria for identifying risks and threats) and in combining human rights protection with market indicators: job exposure, Beveridge curve dynamics, and the share of AI-adjacent roles. The practical significance lies in a calibrated social mix: the application of a universal basic income as a basic security mechanism, targeted employment subsidies, and, in the event of the widespread use of artificial intelligence to critical levels, the introduction of guaranteed employment programmes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.010 |
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".