Taxing Artificial Intelligence

• URL: http://arxiv.org/abs/2607.02144v1
• Date: Thu, 02 Jul 2026 13:21:27 GMT
• Title: Taxing Artificial Intelligence
• Abstract summary: Taxation is an integral part of policy design, and recent academic, industry, and policy debates have begun to consider whether tax instruments can help address these harms.<n>In the AI context, taxation can also correct harmful activity, redistribute unevenly borne costs and gains, and fund regulatory capacity.<n>We discuss the main externalities associated with AI and survey possible tax instruments, including corporate income and rent-based taxes, consumption taxes on AI-related services, and excise instruments tied to specific AI activities.
• Score: 0.4140748098874573
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While AI promises major benefits, its development and deployment can shift costs onto others, including environmental pressures on local communities, labor and creative displacement, and systemic risks from rapid frontier development. Taxation is an integral part of policy design, and recent academic, industry, and policy debates have begun to consider whether tax instruments can help address these harms. In this paper, we explore the viability of AI taxation. More broadly, AI taxation should not be understood only as Pigouvian correction. In the AI context, taxation can also correct harmful activity, redistribute unevenly borne costs and gains, and fund regulatory capacity. We discuss the main externalities associated with AI and survey possible tax instruments, including corporate income and rent-based taxes, consumption taxes on AI-related services, and excise instruments tied to specific AI activities. We further assess the benefits and pitfalls of these instruments, including feasibility, measurement problems, incidence, leakage, and innovation costs. Because AI externalities differ in nuanced ways, tax policy must be carefully designed and matched to the specific harms and policy objectives.

Related papers

• Token Taxes: mitigating AGI's economic risks [11.774079419317273]
  Development of AGI threatens to erode government tax bases, lower living standards, and disempower citizens.<n>We argue that the economic risks posed by a post-AGI world can be effectively mitigated by token taxes.
  arXiv  Detail & Related papers  (2026-03-04T19:38:29Z)
• The California Report on Frontier AI Policy [110.35302787349856]
  Continued progress in frontier AI carries the potential for profound advances in scientific discovery, economic productivity, and broader social well-being.<n>As the epicenter of global AI innovation, California has a unique opportunity to continue supporting developments in frontier AI.<n>Report derives policy principles that can inform how California approaches the use, assessment, and governance of frontier AI.
  arXiv  Detail & Related papers  (2025-06-17T23:33:21Z)
• AI-Enabled Rent-Seeking: How Generative AI Alters Market Transparency and Efficiency [7.630624512225164]
  generative artificial intelligence (AI) has transformed the information environment, creating both opportunities and challenges.<n>This paper explores how generative AI influences economic rent-seeking behavior and its broader impact on social welfare.
  arXiv  Detail & Related papers  (2025-02-18T15:40:22Z)
• Do Responsible AI Artifacts Advance Stakeholder Goals? Four Key Barriers Perceived by Legal and Civil Stakeholders [59.17981603969404]
  The responsible AI (RAI) community has introduced numerous processes and artifacts to facilitate transparency and support the governance of AI systems. We conduct semi-structured interviews with 19 government, legal, and civil society stakeholders who inform policy and advocacy around responsible AI efforts. We organize these beliefs into four barriers that help explain how RAI artifacts may (inadvertently) reconfigure power relations across civil society, government, and industry.
  arXiv  Detail & Related papers  (2024-08-22T00:14:37Z)
• A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms [2.411037855204708]
  This paper introduces a collaborative, human-centred taxonomy of AI, algorithmic and automation harms. We argue that existing, while valuable, can be narrow, unclear, and often overlook the needs of the wider public.
  arXiv  Detail & Related papers  (2024-07-01T13:47:53Z)
• Particip-AI: A Democratic Surveying Framework for Anticipating Future AI Use Cases, Harms and Benefits [54.648819983899614]
  General purpose AI seems to have lowered the barriers for the public to use AI and harness its power. We introduce PARTICIP-AI, a framework for laypeople to speculate and assess AI use cases and their impacts.
  arXiv  Detail & Related papers  (2024-03-21T19:12:37Z)
• The Journey to Trustworthy AI: Pursuit of Pragmatic Frameworks [0.0]
  This paper reviews Trustworthy Artificial Intelligence (TAI) and its various definitions.<n>We argue against using terms such as Responsible or Ethical AI as substitutes for TAI.<n>Instead, we advocate for approaches centered on addressing key attributes and properties such as fairness, bias, risk, security, explainability, and reliability.
  arXiv  Detail & Related papers  (2024-03-19T08:27:04Z)
• Computing Power and the Governance of Artificial Intelligence [51.967584623262674]
  Governments and companies have started to leverage compute as a means to govern AI. compute-based policies and technologies have the potential to assist in these areas, but there is significant variation in their readiness for implementation. naive or poorly scoped approaches to compute governance carry significant risks in areas like privacy, economic impacts, and centralization of power.
  arXiv  Detail & Related papers  (2024-02-13T21:10:21Z)
• An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
  This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
  arXiv  Detail & Related papers  (2021-05-07T12:01:31Z)
• The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies [119.07163415116686]
  We train social planners that discover tax policies that can effectively trade-off economic equality and productivity. We present an economic simulation environment that features competitive pressures and market dynamics. We show that AI-driven tax policies improve the trade-off between equality and productivity by 16% over baseline policies.
  arXiv  Detail & Related papers  (2020-04-28T06:57:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.