TAX LEVERS FOR A SAFER AI FUTURE

2025ssrn-5181207contractsAIlaw

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Abstract

Professor Yonathan Arbel of the University of Alabama School of Law argues that a "capability-safety gap" in AI development, where private firms reap rewards while society bears risks, creates a social misalignment. He proposes using tax policy to address this by re-conceptualizing R&D credits to incentivize safety research, offering consumer credits for safe AI, imposing penalties for non-compliance, and redistributing penalty revenue. This approach aims to embed safety imperatives directly into the economic architecture of AI development, aligning private profit with social welfare.

Citation

APA: Yonathan Arbel. (2025). TAX LEVERS FOR A SAFER AI FUTURE. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5181207

Bluebook: Yonathan Arbel, TAX LEVERS FOR A SAFER AI FUTURE, 2025, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5181207.

Summary (English)

Here's the bullet list summary for 'ssrn-5181207' by Professor Yonathan Arbel:

1.  ## TL;DR
    Professor Yonathan Arbel of the University of Alabama School of Law argues that a "capability-safety gap" in AI development, where private firms reap rewards while society bears risks, creates a social misalignment. He proposes using tax policy to address this by re-conceptualizing R&D credits to incentivize safety research, offering consumer credits for safe AI, imposing penalties for non-compliance, and redistributing penalty revenue. This approach aims to embed safety imperatives directly into the economic architecture of AI development, aligning private profit with social welfare.

2.  ## Section Summaries

    ## The Capability-Safety Gap and the Case for Tax Intervention
    Professor Yonathan Arbel of the University of Alabama School of Law writes that a dangerous "capability-safety gap" in AI is widening, as developers gain private rewards while society bears cascading risks, creating a social misalignment. Professor Yonathan Arbel of the University of Alabama School of Law writes that fiscal policy, specifically taxation, offers a powerful and adaptable tool to embed safety imperatives into AI's economic architecture, aiming to align private profit with social welfare and address the urgent need for intervention due to traditional regulatory failures and the collapse of industry self-regulation.

    ## Business Tax-Incentives for Investments in AI Safety
    Professor Yonathan Arbel of the University of Alabama School of Law writes that specific business tax-incentives, such as credits or enhanced deductions, should directly encourage substantial corporate investments in AI safety research, development, and deployment. Professor Yonathan Arbel of the University of Alabama School of Law writes that these fiscal mechanisms, including a proposed "AI Safety Research Tax Credit" and adjusted expensing rules favoring safety R&D (like testing and alignment) over pure capability enhancements, aim to make safety-enhancing activities economically attractive, addressing underinvestment by aligning financial interests with societal well-being.

    ## Consumer-Side Incentives and Market-Based Certification
    Professor Yonathan Arbel of the University of Alabama School of Law writes that consumer-side incentives are crucial for fostering a market for safe AI, proposing an "AI Reliability Credit" for consumers purchasing AI products certified as reliable and safe, mirroring energy efficiency tax incentives. Professor Yonathan Arbel of the University of Alabama School of Law writes that this credit would incentivize producers to pursue rigorous safety certifications (e.g., for bias mitigation, data protection), thereby spurring consumer demand for safer AI and aligning private firm incentives with societal goals through market-based certification.

    ## Corrective Taxes and Penalties for Non-Compliance
    Professor Yonathan Arbel of the University of Alabama School of Law writes that to penalize unsafe AI development, corrective Pigouvian taxes should make firms internalize the external harms they create. Professor Yonathan Arbel of the University of Alabama School of Law writes that a comprehensive penalty framework, featuring graduated penalties like tax surcharges and benefit recapture for AI posing public safety risks, aims to internalize social costs, create strong ex ante incentives for responsible innovation, and ensure public funds do not subsidize harmful AI, building on precedents where tax benefits are compliance-contingent.

    ## Administrative Advantages and Challenges of a Tax-Based Approach
    Professor Yonathan Arbel of the University of Alabama School of Law writes that tax policy offers distinctive advantages for AI safety, harnessing existing institutional frameworks like the IRS, preserving market dynamics, and potentially reshaping organizational culture. Professor Yonathan Arbel of the University of Alabama School of Law writes that while challenges include political economy concerns and distinguishing genuine safety from "safety-washing," his framework suggests the IRS leverage its R&D evaluation experience, mandate detailed safety documentation, and use emerging industry benchmarks to address these issues and effectively mobilize private sector expertise for AI safety.

One-page summary

# TAX LEVERS FOR A SAFER AI FUTURE — one-page summary

**Paper ID:** `ssrn-5181207`
**Year:** 2025
**Author(s):** Yonathan Arbel
**SSRN:** https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5181207

## TL;DR

Professor Yonathan Arbel of the University of Alabama School of Law argues that a "capability-safety gap" in AI development, where private firms reap rewards while society bears risks, creates a social misalignment. He proposes using tax policy to address this by re-conceptualizing R&D credits to incentivize safety research, offering consumer credits for safe AI, imposing penalties for non-compliance, and redistributing penalty revenue. This approach aims to embed safety imperatives directly into the economic architecture of AI development, aligning private profit with social welfare.

## Keywords

contracts; AI; law

## Files

- Full text: `papers/ssrn-5181207/paper.txt`
- PDF: `papers/ssrn-5181207/paper.pdf`
- Summary (EN): `papers/ssrn-5181207/summary.md`
- Summary (ZH): `papers/ssrn-5181207/summary.zh.md`

_Auto-generated study aid. For canonical content, rely on `paper.txt`/`paper.pdf`._

Study pack

# Study pack: TAX LEVERS FOR A SAFER AI FUTURE (ssrn-5181207)

- SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5181207
- Full text: `papers/ssrn-5181207/paper.txt`
- Summary (EN): `papers/ssrn-5181207/summary.md`
- Summary (ZH): `papers/ssrn-5181207/summary.zh.md`

## Elevator pitch

Professor Yonathan Arbel of the University of Alabama School of Law argues that a "capability-safety gap" in AI development, where private firms reap rewards while society bears risks, creates a social misalignment. He proposes using tax policy to address this by re-conceptualizing R&D credits to incentivize safety research, offering consumer credits for safe AI, imposing penalties for non-compliance, and redistributing penalty revenue. This approach aims to embed safety imperatives directly into the economic architecture of AI development, aligning private profit with social welfare.

## Keywords / concepts

contracts; AI; law

## Suggested questions (for RAG / study)

- What is the paper’s main claim and what problem does it solve?
- What method/data does it use (if any), and what are the main results?
- What assumptions are doing the most work?
- What are the limitations or failure modes the author flags?
- How does this connect to the author’s other papers in this corpus?

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摘要（中文）

以下为 Yonathan Arbel 教授关于“ssrn-5181207”研究的要点总结：

1.  ## 核心观点
    阿拉巴马大学法学院的 Yonathan Arbel 教授认为，人工智能发展中存在的“能力-安全鸿沟”（即私营企业获得回报，而社会承担风险）造成了社会失调。他提议通过税收政策来解决此问题，具体措施包括：重新定义研发税收抵免以激励安全研究；为购买安全人工智能产品的消费者提供税收抵免；对违规行为处以罚款；并将罚款收入进行再分配。此方法旨在将安全准则直接嵌入人工智能发展的经济架构中，从而使私人利润与社会福祉相一致。

2.  ## 各章节摘要

    ## 能力-安全鸿沟与税收干预的理由
    阿拉巴马大学法学院的 Yonathan Arbel 教授指出，人工智能领域危险的“能力-安全鸿沟”正在扩大，开发者获得私人回报，而社会则承担着级联风险，这造成了社会失调。Arbel 教授认为，财政政策，特别是税收，提供了一种强大且适应性强的工具，可将安全准则嵌入人工智能的经济架构中，旨在使私人利润与社会福祉相一致，并应对因传统监管失灵和行业自律崩溃而产生的紧迫干预需求。

    ## 针对人工智能安全投资的企业税收激励
    阿拉巴马大学法学院的 Yonathan Arbel 教授认为，应通过特定的企业税收激励措施（如税收抵免或增强型费用扣除）直接鼓励企业在人工智能安全研究、开发和部署方面进行大量投资。Arbel 教授指出，这些财政机制，包括拟议的“人工智能安全研究税收抵免”以及调整后的、优先支持安全研发（如测试与对齐）而非单纯能力增强的费用化规则，旨在使增强安全的活动在经济上具有吸引力，通过协调财务利益与社会福祉来解决投资不足的问题。

    ## 消费者端激励与基于市场的认证
    阿拉巴马大学法学院的 Yonathan Arbel 教授认为，消费者端激励对于培育安全人工智能市场至关重要。他提议设立“人工智能可靠性税收抵免”，为购买经认证可靠且安全的人工智能产品的消费者提供税收优惠，这与能源效率税收激励措施类似。Arbel 教授指出，这种税收抵免将激励生产者寻求严格的安全认证（例如，偏见缓解、数据保护认证），从而刺激消费者对更安全人工智能的需求，并通过基于市场的认证使私营企业激励与社会目标相一致。

    ## 纠正性税收与违规处罚
    阿拉巴马大学法学院的 Yonathan Arbel 教授认为，为惩罚不安全的人工智能开发行为，应采用纠正性庇古税，使企业将其造成的外部损害内部化。Arbel 教授指出，一个全面的处罚框架——包括针对构成公共安全风险的人工智能设定分级处罚（如税收附加费和优惠追回）——旨在内部化社会成本，为负责任的创新建立强有力的事前激励，并确保公共资金不被用于补贴有害的人工智能。该框架借鉴了税收优惠以合规为条件的相关先例。

    ## 基于税收的方法的行政优势与挑战
    阿拉巴马大学法学院的 Yonathan Arbel 教授认为，税收政策在人工智能安全方面具有独特优势，它能利用现有制度框架（如美国国税局），保持市场动态，并可能重塑组织文化。Arbel 教授指出，尽管面临政治经济学方面的顾虑以及区分真正安全与“安全清洗”（或称“伪安全”）等挑战，但其框架建议美国国税局利用其研发评估经验，强制要求提供详细的安全文件，并采用新兴行业基准来解决这些问题，从而有效动员私营部门的专业知识以促进人工智能安全。