I Robot: U Tax? Considering the Tax Policy Implications of Automation
Bibliographic record
Abstract
In a 2017 interview, Microsoft founder Bill Gates recommended taxing robots to slow the pace of automation. Funds raised could be used to retrain and financially support displaced workers. Up to 47 per cent of US jobs are at risk by advancements in artificial intelligence. Low-wage workers currently hold a majority of those at-risk jobs. Increased automation is likely to exacerbate income inequality. While employment changes due to automation are not new, advances in artificial intelligence threaten to eliminate many more jobs than were eliminated historically through automation. Accelerated automation presents two problems: a revenue problem and a human problem. The revenue problem exists because the tax system is designed to tax labour more heavily than capital, as labour is less likely to be able to avoid taxation. Capital investment, on the other hand, is taxed more lightly because capital is mobile and can escape taxation. When capital becomes labour, as in automation, the bottom falls out of the system. The human problem is first that most people need income from working to survive. Some scholars have advocated for a governmentally provided universal basic income (UBI). Taxing robots could in theory provide revenue for a UBI, although any source of revenue would work just as well. While a UBI would solve the survival problem, humans need more than basic survival. In his classic work, psychologist Abraham Maslow listed survival as the foundation of his hierarchy of needs. Work satisfies the higher order needs of social identity and self-esteem. The Tax Cuts and Jobs Act (TCJA), enacted in December 2017, significantly cut the US corporate tax rate, from 35 per cent to 21 per cent. In addition, TCJA increased tax benefits for purchasing equipment (which would include automation) by significantly enhancing bonus depreciation. The new tax legislation continued and deepened the existing tax bias towards automation. This article explores policy options for solving the revenue problem and the "jobs" problem, including a discussion and critique of UBI proposals and recommendations for other policy options, such as an enhanced earned income tax credit, incentives for employers, and reviving an idea from the Great Depression, the Civilian Conservation Corps.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.016 | 0.018 |
| Insufficient payload (model declined to judge) | 0.009 | 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".