Orly Lobel (U San Diego Law) has posted “The Future of Work in the Era of AI” (Indiana Law Journal, Vol. 100, No. 1 (2024)) on SSRN. Here is the abstract:

Artificial intelligence (AI) is revolutionizing both work itself and the processes of employment—hiring, recruitment, evaluation, compensation, performance analysis, retention, and job mobility. This Essay, based upon the 2024 Indiana Law Journal annual William R. Stewart Lecture, examines the effects of AI on work and argues for a holistic approach that harnesses the benefits of automation while addressing the inevitable systemic changes that AI is rapidly bringing to the labor market. The Essay examines two industries in which AI is already changing labor market demands: trucking and the performing arts. The Essay argues that while the automation can often increase efficiency and productivity, as well as accuracy and fairness in the labor market, inevitably the rapid acceleration of AI integration will bring significant disruptions. Policymakers should separately address the emergence of new forms of inequities, the necessity for reskilling, and the need to establish more robust economic security safeguards that are not dependent on fulltime, continuous employment for all. The Essay thus considers how a more equitable tax framework, publicly funded reskilling programs, safety nets like Universal Basic Income (UBI), and a proactive reimagining of work can help displaced workers adapt, thrive, and contribute to the evolving economy.

Amedeo Rizzo (U Oxford Law) and Giorgio Hassan (SDA Bocconi) have posted “AI Risk Management in Tax Audits: A Comparative Review of the EU and US Regulatory Approaches” on SSRN. Here is the abstract:

This paper focuses on the AI risk management framework that applies to tax authorities under the EU and US legal systems. In recent years, the development of AI has entered the field of tax administration, revolutionizing the planning and operational tasks of tax authorities. In this scenario, it is crucial that taxpayers are not unduly exposed to any risk of harm arising from the unsafe implementation of AI by tax authorities. In this regard, the EU legal framework – with the GDPR and the recent AI Act – and the US legal framework – with the recent Executive Order on the development of Safe, Secure, and Trustworthy AI – provide valuable sources of risk-based obligations that could adequately address the risks of AI in the tax domain.

On the EU side, the GDPR and AI Act have a complementary approach – a “rights-based approach” in the case of the GDPR, and a “risk-based approach” in the case of the AI Act – and an overlapping scope of application. In the field of AI risk management, the potential overlap between the GDPR and the AI Act may provide valuable indications for adapting the GDPR-based risk management framework to the realm of AI, and, at the same time, for interpreting the scope of the AI Act in light of the rights provided under the GDPR. On the US side, the risk management obligations stemming from the Executive Order on Safe AI draw from the recent developments in AI regulation in the EU, providing measures that have a similar scope to the requirements of the AI Act. From this perspective, we discuss that the EU and US approaches to AI regulation are slowly aligning and are similarly able to address the risks arising from the use of AI in the tax domain – such as, particularly, the risks concerning AI-enabled discrimination and human-AI interaction. However, both in the EU and the US, it is unclear whether the risk management framework provided by these regulations can effectively extend to tax authorities. Except for the GDPR, the AI Act and the Executive Order seem to consider tax-related AI systems at a lower risk class compared to other categories of “high-risk” or “risk-impacting” AI systems. The misalignment in the classification of tax-related AI systems could jeopardize the application of the AI risk management framework provided in these regulations, and consequently, expose taxpayers to significant risks of harm.

For this reason, we argue that the risks concerning the use of AI in tax administration, and the benefits that could derive from the adoption of a risk management framework inspired by these three regulations, should convince EU and US lawmakers to adopt a precautionary and uniform approach to the risk categorization of tax-related AI systems. Particularly, lawmakers should locate tax-related AI systems among the pool of high-risk and rights-impacting systems for the purposes of the AI Act and the Executive Order, for the better interest of taxpayers in the EU and the US.

John Nay (Stanford Codex; NYU) et al. have posted “Large Language Models as Tax Attorneys: A Case Study in Legal Capabilities Emergence” on SSRN. Here is the abstract:

Better understanding of Large Language Models’ (LLMs) legal analysis abilities can contribute to improving the efficiency of legal services, governing artificial intelligence, and leveraging LLMs to identify inconsistencies in law. This paper explores LLM capabilities in applying tax law. We choose this area of law because it has a structure that allows us to set up automated validation pipelines across thousands of examples, requires logical reasoning and maths skills, and enables us to test LLM capabilities in a manner relevant to real-world economic lives of citizens and companies. Our experiments demonstrate emerging legal understanding capabilities, with improved performance in each subsequent OpenAI model release. We experiment with retrieving and utilising the relevant legal authority to assess the impact of providing additional legal context to LLMs. Few-shot prompting, presenting examples of question-answer pairs, is also found to significantly enhance the performance of the most advanced model, GPT-4. The findings indicate that LLMs, particularly when combined with prompting enhancements and the correct legal texts, can perform at high levels of accuracy but not yet at expert tax lawyer levels. As LLMs continue to advance, their ability to reason about law autonomously could have significant implications for the legal profession and AI governance.

Jay A. Soled (Rutgers University) and Kathleen DeLaney Thomas (UNC School of Law) have posted “AI, Taxation, and Valuation” (Iowa Law Review, Forthcoming 2023) on SSRN. Here is the abstract:

Virtually every tax system relies upon accurate asset valuations. In some cases, this is an easy identification exercise, and the exact fair market value of an asset is readily ascertainable. Often, however, the reverse is true, and ascertaining an asset’s fair market value yields, at best, a numerical range of possible outcomes. Taxpayers commonly capitalize upon this uncertainty in their reporting practices, such that tax compliance lags and the IRS has a difficult time fulfilling its oversight responsibilities. As a by-product of this dynamic, the Treasury suffers.

This Article explores how tax systems, utilizing artificial intelligence, can strategically address asset-valuation concerns, offering practical reforms that would help obviate this nettlesome and age-old problem. Indeed, if the IRS and Congress were to take advantage of this new and innovative technological approach, doing so would bode well for more accurate asset valuations and thereby foster greater tax compliance. Put somewhat differently, in the Information Era in which we exist, it is simply no longer true that accurate asset valuations are unattainable.

Amanda Parsons (Columbia Law School) has posted “Tax’s Digital Labor Dilemma” on SSRN. Here is the abstract:

Digitalization has reshaped the relationship between companies and their customers and users. Customers and users increasingly serve a dual role. They are not only consumers but also producers, creating content and data. They are a value-creating workforce, functioning as “digital laborers.” Under the current U.S. international tax system, the presence of digital laborers in a country does not grant that country taxing rights over income stemming directly from those digital laborers’ content and data creation. As a result, what are essentially the same business activities—workforces creating products and performing services for a company—are taxed differently when they are performed by digital laborers rather than a traditional workforce. This inconsistency and the accompanying outcome that countries cannot tax corporate income arising from extensive business activities within their borders has led to cries that the current system is unfair.

Recent reforms addressing this outcome, including digital services taxes and proposals granting taxing authority over residual profits to market jurisdictions, most notably the OECD Pillar One Blueprint, share a common weakness. They do not recognize the function of digital laborers as producers in the modern economy. As a result, they overturn the theory of source-based taxation as a taxing right granted to the country of production, not the country of consumption, as well as introduce major structural changes to the international tax system—all to correct an unfairness that can be remedied under the system’s current theoretical framework and structure.

This Article rejects the notion that these major theoretical and structural changes are necessary or even an appropriate method to allow digital laborers’ home countries to tax income directly related to their work. Instead, the international tax system should recognize digital laborers’ role as a new type of workforce for companies and, accordingly, allow their home countries to tax income related to their work under the existing application of the source principle and with more incremental structural reforms. In addition to minimizing disruption in international tax law, this approach brings a return of coherence and a sense of fairness by taxing equivalent economic activities equivalently.

Michelle Lyon Drumbl (Washington and Lee University School of Law) has posted “#Audited: Social Media and Tax Enforcement” (Oregon Law Review, Forthcoming) on SSRN. Here is the abstract:

With limited resources and a diminished budget, it is not surprising that the Internal Revenue Service would seek new tools to maximize its enforcement efficiency. Automation and technology provide new opportunities for the IRS, and in turn, present new concerns for taxpayers. In December 2018, the IRS signaled its interest in a tool to access publicly available social media profiles of individuals in order to “expedite IRS case resolution for existing compliance cases.” This has important implications for taxpayer privacy.

Moreover, the use of social media in tax enforcement may pose a particular harm to an especially vulnerable population: low-income taxpayers. Social science research shows us that the poor are already over-surveilled, and researchers have identified various ways in which algorithmic screening and data mining can result in discrimination. What, then, are the implications of social media mining in the context of tax enforcement, especially given that the IRS already audits the poor at a rate similar to which it audits the highest earning individuals? How can these concerns be reconciled with the need for tax enforcement?

This article questions the appropriateness of the IRS further automating its enforcement tactics in ways that may harm already vulnerable individuals, makes proposals to balance the use of any such tactics with respect for taxpayer rights, and considers how tax lawyers should advise their clients in an era of diminishing privacy.

Recommended.

 

Abraham Sutherland (University of Virginia School of Law) has posted “Tax Treatment of Block Rewards: A Primer” on SSRN. Here is the (bulleted) abstract:

• An unsettled issue of immense practical and economic importance: how to tax the new “reward tokens” created in public cryptocurrency networks.

• The wrong policy would drive innovation elsewhere. Fortunately, the correct policy is mandated by existing law: these new tokens – like all forms of new property – do not give rise to income until they are sold.

• Informal, seven-year-old IRS guidance – not law – geared to Bitcoin and proof of work suggests reward tokens are immediate income at their fair market value on the date “received.”

• For the newer proof-of-stake technology, this would create a compliance nightmare and punitive overtaxation.

• Ethereum, Tezos, Cosmos, and many other proof-of-stake cryptocurrencies: compliance would be a daunting task – for the IRS as well as taxpayers. New taxable events would occur every few seconds.

• “Income” under such a policy would overstate taxpayers’ actual economic gain – significantly, in many cases – resulting in demonstrable, systematic overtaxation.

• The overtaxation is equivalent to taxing a 21 for 20 stock split by counting the “new” share – that is, 20/21 the value of an old share – as taxable income.

• Like any property, cryptocurrency tokens can indeed be “income” – when received as payment or as compensation.

• But new property – property created or discovered by a taxpayer, not received as payment or compensation from someone else – is never income, and never has been.

• Cattle, corn, gold, widgets, wild truffles, artworks, novels – think of any new property created or discovered by the taxpayer: It’s no one else’s expense, and it’s not income until sold.

• As a factual matter, new reward tokens are indeed created by stakers.

• Understanding how tokens are created is complicated; resorting to flawed financial analogies is easy.

• Block rewards are nothing like “stock dividends.” They are not “compensation for services” – try to imagine taxable “compensation” that doesn’t come from another person.

• Reward tokens cannot be taxed as immediate income under section 61 of the Internal Revenue Code. But not to worry: they’ll be fairly taxed when sold.

• Policy problems remain for cryptocurrency taxation – fortunately, new reward tokens are not among them.

• It’s not too late to clarify this issue before the effects of a wrong or uncertain policy are felt by millions of taxpayers and the IRS alike.

Vincent Ooi (Singapore Management University – School of Law and Centre for AI & Data Governance) has posted “Adapting Taxation for the Digital Economy in Singapore” ((2021) 27(1) Asia-Pacific Tax Bulletin 1-10) on SSRN. Here is the abstract:

The advent of the digital economy has had profound implications for taxation. Tax systems have been forced to adapt as they become increasingly unsuited for the realities of modern commerce. While Singapore has largely followed international developments, particularly in the area of international taxation, it has often made numerous innovative policy decisions in line with its national interests. The various policy decisions which Singapore has made on taxing the digital economy span both international and domestic tax. In the area of domestic tax, the examples have been further divided by subject matter, like e-commerce, digital tokens, automation, and electronic instruments. Other jurisdictions will face similar choices when considering how to adapt their domestic tax systems for the digital economy, and the tax policy decisions made by a small, highly-open economy such as Singapore may provide insights to jurisdictions seeking to adapt their tax systems for the digital economy.