Taxing Automation in Africa: Balancing Innovation and Socio-Economic Equality in the Fourth Industrial Revolution
Bibliographic record
Abstract
The implementation of artificial intelligence (AI) and robotics is transforming nations worldwide, triggering discussion about their socio-economic impacts and appropriate regulatory responses. While extensively debated in developed economies, this issue remains underexplored in Africa- a region facing unique developmental challenges and opportunities. This systematic review explores the implications of automation, robotics and AI on income inequality, employment and taxation policies, focusing on literature published between 2017 and 2024. For this purpose, as search was carried out in Scopus and Google Scholar databases. A total of 78 papers were found, and after analysing them according to the PRISMA Statement 2020, a total of 36 papers were selected. The review indicates that automation and AI excessively impact low-skilled employees, worsening income disparity, while high-skilled workers benefit from increased salaries. Moreover, analysis indicates that robot taxation and investment in higher education are potential interventions to mitigate these adverse socio-economic effects of technological innovation. The review suggests that government and policymakers should consider tax policies to fund educational institutions to equip citizens with the skills needed in the digital age. The paper offers practical insights for policymakers on robot taxation and labour force and advances understanding by proposing a framework for addressing automation-driven inequality internationally.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".