Taxing Artificial Intelligence and Robots: Critical Assessment of Potential Policy Solutions and Recommendation for Alternative Approaches
Bibliographic record
Abstract
In recent years, investments in technology have resulted in an exponential growth of AI/robots. It is argued that some of these innovations are able to outperform and replace humans in various types of jobs. Accordingly, concerns regarding government revenues have been raised, as AI/robots could trigger widespread unemployment with the result that less tax revenue will accrue to the government. This contribution, as a start, analyses whether or not this is truly a concern. In order to do so, the authors map the Industrial Revolution(s) that humankind has witnessed and then conduct a literature review of economic and demographic studies relevant to the debate. The economic studies indicate two different directions, that is, some argue that AI/robots (Industry 4.0) will increase human jobs whereas others argue that jobs could disappear. At the same time, the demographic perspective indicates that a purely economic employment-focused view of AI/robots is bound to lead to inconclusive results. Assuming that this is a probable concern, the authors summarize selected measures taken by governments as well as the various options that have been considered in academic literature to introduce taxes on AI/robots. Subsequently, the authors analyse the various “taxing” options from the perspective of commonly accepted tax policy principles applicable to electronic commerce (Ottawa Taxation Framework conditions). This analysis indicates that several proposals (e.g. proposals that treat AI/robots as independently taxable subjects or proposals that attribute income to owners of AI/robots) breach the principles of (i) neutrality; (ii) simplicity and certainty; (iii) efficiency; (iv) effectiveness and fairness; and (v) flexibility. Thus, such measures should not be pursued. The authors also conclude that, at this stage, targeted taxes on AI/robots should not be introduced, as this would also be contrary to the measures taken by governments globally to promote research and development (R&D) (input or output incentives). The present contribution therefore suggests that governments need to be proactive rather than reactive in this area. This could be achieved by monitoring the impact of AI/robots on a regular basis, and if the trend indicates that jobs are disappearing or revenues are declining, then the article suggests that states raise funds from an earmarked education tax. The funds raised from this tax, among other objectives, should be used to finance and foster professional educational programmes to reskill workers, besides assisting and guiding them to transition into new roles. However, a national measure may not be sufficient to tackle the issue (issues) at stake, especially in light of the demographic perspective discussed in the contribution. Thus, considering some jurisdictions may not be in a position to implement or fully benefit from an education tax, the authors also discuss the possibility of implementing a global fiscal redistribution mechanism (multilateral solution) from developed (surrender jurisdictions) to developing countries (recipient jurisdictions). The latter, depending on its scope, could be in the form of a global education tax or more broadly a planetary tax.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".