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Record W3166775709 · doi:10.59403/2sjxe4d

Taxing Artificial Intelligence and Robots: Critical Assessment of Potential Policy Solutions and Recommendation for Alternative Approaches

2020· article· en· W3166775709 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueWorld Tax Journal · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Economy and Work Transformation
Canadian institutionsnot available
Fundersnot available
KeywordsRobotGovernment (linguistics)RevenueTax revenueNeutralityOrder (exchange)UnemploymentSimplicityEconomicsBusinessArtificial intelligencePublic economicsComputer sciencePolitical scienceLawFinanceEconomic growth

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.985
Threshold uncertainty score0.386

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.178
GPT teacher head0.372
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it