Michael R. Siebecker (U Denver Sturm College Law) has posted “Reconceiving Corporate Rights and Regulation in the AI Era” on SSRN. Here is the abstract:

Can existing corporate governance principles properly guide the relationship between shareholders and directors as artificial intelligence (“AI”) plays an increasingly prominent role in corporate management, planning, and operations? Without a doubt, AI technologies allow corporations to enjoy enhanced efficiency and innovation. But the vast range of AI capabilities-from sophisticated data analytics to autonomous decision-making-raises profound questions about whether traditional governance principles remain sufficiently robust to cabin the proper development and deployment of such a powerful and rapidly evolving set of new technologies. Current corporate governance structures that focus on human actors and traditional business decision-making mechanisms seem ill-suited to address some of the novel legal questions that increased reliance on AI poses, especially considering the opacity regarding how AI technologies actually function. Because the existing fiduciary framework for corporate governance remains insufficiently supple to accommodate AI’s transformative impact on corporate practices and strategy, there is a pressing need to reconsider basic corporate governance principles. 

Shaping appropriate corporate legal constructs to guide the development and dissemination of AI technologies will most likely require a multifaced approach involving new legislative enactments, reconsideration of existing common law principles, and regulatory reforms. Without this concerted approach, striking a sustainable balance between protecting the public interest and fostering innovation becomes far too precarious and uncertain. AI technologies offer incredible opportunities for economic growth, enhanced efficiency, and revolutionary innovation in the corporate realm. But enhanced reliance on algorithmic decision-making also raises troubling concerns regarding corporate accountability, transparency, and threats to important social and civic institutions. As a result, a holistic reconsideration of corporate rights and responsibilities seems essential to ensure a proper balancing between the manifold benefits AI advancements might produce and the continued integrity of public institutions and civic values. By examining the potential disconnect between AI advancements and current corporate governance standards, this Article uncovers the shortcomings of existing corporate jurisprudence and advances a set of principles for guiding the articulation of a more dynamic and responsible corporate governance approach to AI.

Leo E. Strine, Jr. (Wachtell; U Penn Law) has posted “Using Experience Smartly to Ensure a Better Future: How the Hard-Earned Lessons of History Should Shape The External and Internal Governance of Corporate Use of Artificial Intelligence” on SSRN. Here is the abstract:

Artificial intelligence or “AI” has transformative potential. But that reality should not obscure the fact that our society has longstanding experience with the corporate development of novel technologies that pose the simultaneous potential to better human lives and to create massive harm. This article, prepared for the occasion of the 50th anniversary of the Journal of Corporate Law and for the Rome Conference on AI, Ethics, and the Future of Corporate Governance, looks backward at the prior experience with corporate profit-seeking through the development and use of transformative technologies to suggest policy measures that might help ensure that the benefits of AI’s development by for-profit business entities to society far exceed its downside.

Chen Wang (UC Berkeley Law) and Xu Ke (Renmin U) have posted “Toward Digital Corporate Law: Revisiting Corporate Law’s Responses to Technology” on SSRN. Here is the abstract:

This article explores the dynamic interplay between emerging technologies and corporate law, questioning whether these advancements necessitate a fundamental reshaping of core legal doctrines. It delves into specific areas like corporate formation, governance, and finance, through a comparative lens examining Chinese and U.S. laws and regulations. The article focuses on the capacity of modern frameworks of corporate law to address challenges posed by technologies like AI, particularly concerning the evolution of corporate agents’ fiduciary duties and the balance of power between shareholders and management. It proposes innovative approaches to developing future corporate law, stressing enhanced compatibility and adaptability with technological progress, such as contemplating data as a corporate asset and allowing for the issuance and storage of stocks in digital form. The article also explores balancing shareholder, stakeholder, and societal interests in this evolving landscape, including the use of AI in fulfilling corporation’s ESG responsibilities and the potential for fund managers to employ AI to make informed proxy voting decisions. By posing thought-provoking questions for future research, it aims to stimulate a nuanced dialogue on the critical intersection of law and technology, particularly in the context of the increasing digitization of corporate law and the potential for using software engineering technics to improve legislating and rulemaking.

Yuning Li (Peking University School of Transnational Law) has posted “Trusted and Trustworthy Algorithmic Fiduciaries” on SSRN. Here is the abstract:

The Digital Age is speedily transforming into the Algorithmic Age, where algorithms carry on or contribute to a significant number of important social, economic, and technical decision-making. Apart from platform dominance, private governance, and surveillance capitalism, private and public use of algorithmic decision-making faces an increasingly severe legitimacy crisis of not being trusted or trustworthy. Three phases of Algorithmic Fiduciaries will accompany us into the ultimate human-centered symbiosis relations individually between human and intelligent algorithms. Trusted and trustworthy algorithmic fiduciaries are vitally important in maximizing the joint potentials and capabilities to significantly benefit the algorithms and the humanities.

This paper consists of three parts. First, it proposes the idea of “algorithmic fiduciary” and argues that the trusted and trustworthy algorithmic fiduciary is important. Second, it researches government policies and regulations, industry norms, civil society proposals, and other actions within the Government-Company-NGO Triangular on algorithmic decision-making in the European Union and the United States. Third, it evaluates how these actions impact trusted and trustworthy algorithmic fiduciaries.

Chen Wang (UC Berkeley – School of Law) has posted “Can ChatGPT Personalize Index Funds’ Voting Decisions?” on SSRN. Here is the abstract:

ChatGPT has risen rapidly to prominence due to its unique features and generalization ability. This article proposes using ChatGPT to assist small investment funds, particularly small passive funds, in making more accurate and informed proxy voting decisions.

Passive funds adopt a low-cost business model. Small passive funds lack financial incentives to make informed proxy voting decisions that align with their shareholders’ interests. This article examines the implications of passive funds on corporate governance and the issues associated with outsourcing voting decisions to proxy advisors. The article finds that passive funds underspend on investment stewardship and outsource their voting proxy decisions to proxy advisors, which could lead to biased or erroneous recommendations.

However, by leveraging advanced AI language models such as ChatGPT, small passive funds can improve their proxy voting accuracy and personalization, enabling them to better serve their shareholders and navigate the competitive market.

To test ChatGPT’s potential, this article conducted an experiment using its zero-shot GPT-4 model to generate detailed proxy voting guidelines and apply them to a real-world proxy statement. The model successfully identified conflicts of interest in the election of directors and generated comprehensive guidelines with weight for each variable. However, ChatGPT has some limitations, such as token limitations, long-range dependencies, and likely ESG inclination.

To enhance its abilities, ChatGPT can be fine-tuned using high-quality, domain-specific datasets. However, investment funds may face challenges when outsourcing voting decisions to AI, such as data and algorithm biases, cybersecurity and privacy concerns, and regulatory uncertainties.

Michael R. Siebecker (U Denver Law) has posted “The Incompatibility of Artificial Intelligence and Citizens United” (Ohio State Law Journal, Vol. 83, No. 6, pp. 1211-1273, 2022) on SSRN. Here is the abstract:

In Citizens United v. FEC, the Supreme Court granted corporations essentially the same political speech rights as human beings. But does the growing prevalence of artificial intelligence (“AI”) in directing the content and dissemination of political communications call into question the jurisprudential soundness of such a commitment? Would continuing to construe the corporation as a constitutional rights bearer make much sense if AI entities could wholly own and operate business entities without any human oversight? Those questions seem particularly important, because in the new era of AI, the nature and practices of the modern corporation are quickly evolving. The magnitude of that evolution will undoubtedly affect some of the most important aspects of our shared social, economic, and political lives. To the extent our conception of the corporation changes fundamentally in the AI era, it seems essential to assess the enduring soundness of prior jurisprudential commitments regarding corporate rights that might no longer seem compatible with sustaining our democratic values. The dramatic and swift evolution of corporate practices in the age of AI provides a clarion call for revisiting the jurisprudential sensibility of imbuing corporations with full constitutional personhood in general and robust political speech rights in particular. For if corporations can use AI data mining and predictive analytics to manipulate political preferences and election outcomes for greater profits, the basic viability and legitimacy of our democratic processes hang in the balance. Moreover, if AI technology itself plays an increasingly important, if not controlling, role in determining the content of corporate political communication, granting corporations the same political speech rights as humans effectively surrenders the political realm to algorithmic entities. In the end, although AI could help corporations act more humanely, the very notion of a corporation heavily influenced or controlled by non-human entities creates the need to cabin at least somewhat the commitment to corporations as full constitutional rights bearers. In particular, with respect to corporate political activity, the growing prevalence of AI in managerial (and possibly ownership) positions makes granting corporations the same political speech rights as humans incompatible with maintaining human sovereignty.

Peder Østbye (Norges Bank) has posted “Exploring Liability for Cryptoeconomic Consensus – A Law and Economics Approach” on SSRN. Here is the abstract:

Cryptoeconomic systems, such as cryptocurrencies and decentralized autonomous organizations, rely on consensus at several levels. Their protocols and the open source code implementing them are often the results of consensus among several participants. The systems are updated according to consensus mechanisms set in their protocols. This consensus is sometimes reliant on consensus among another set of participants in other cryptoeconomic systems, such as oracles feeding a cryptoeconomic system with external information. The outcomes of consensus may be illegitimate or harmful, which raises the question of liability. There is a heated debate around such liability – both as a matter of law and policy. Some call for stricter regulation in terms of harsher liabilities, while others argue for more of a light-touch approach, shielding participants from liability in the name of promoting “responsible innovation.” Some even argue for cryptoeconomic systems to be left to themselves and their own architecture-based self-regulation not subject to national laws. However, when cryptoeconomic consensus results in undesirable outcomes, remedies are often searched for in the law, both in public enforcement and private litigation. This paper utilizes law and economics to explore the merits of legalist approaches to liability for cryptoeconomic consensus, normative policy guidance for such liability, and institutional implications for such liability.

Recommended.

Jun Aoyagi (HKUST) and Yuki Ito (U Cal, Berkeley) have posted “Competing DAOs” on SSRN. Here is the abstract:

A decentralized autonomous organization (DAO) is an entity with no central control and ownership. A group of users discuss, propose, and implement a new platform design with smart contracts on blockchain by taking control away from a centralized platformer. We develop a model of platform competition with the DAO governance structure and analyze how strategic complementarity affects the development of DAOs. Compared to traditional competition between centralized platformers, a DAO introduces an additional layer of competition played by users. Since users are multi-homing, they propose a new platform design by internalizing interactions between platforms and create additional values, which is reflected by the price of a governance token. A platformer can extract this value by issuing a token but must relinquish control of her platform, losing potential fee revenue. Analyzing this tradeoff, we show that centralized platformers tend to be DAOs when strategic complementarity is strong, while an intermediate degree of strategic complementarity leads to the coexistence of a DAO and a traditional centralized platform.

Kelvin F.K. Low (NUS – Faculty of Law), Edmund Schuster (London School of Economics – Law School), and Wai Yee Wan
(City University of Hong Kong) have posted “The Company and Blockchain Technology” (Elgar Handbook on Corporate Liability, forthcoming).

Blockchain and distributed ledger technology (DLT) has generated much excitement over the past decade, with proclamations that it would disrupt everything from elections to finance. Unsurprisingly, the much-maligned corporate form is also considered ripe for disruption. While certainly imperfect, and currently serviced by creaking legal infrastructure premised upon direct shareholdings, are its problems ones of centralization/intermediation? What exactly are the limits of DLT? In this chapter, we propose to expose the ignorance behind the hype that the venerable corporation will either be revitalized by DLT or replaced by Decentralised Autonomous Organisations (DAOs). We will demonstrate that proponents of DLT disruption either overestimate the potential of the technology by taking at face value its claims of security without unpacking what said security entails (and what it does not) or lack awareness of the history of and market demand for intermediation as well as the complexities of modern corporations.

Kirsten Martin (Notre Dame)) and Bidhan Parmar (U Virginia – Darden School of Business) have posted “What Firms Must Know Before Adopting AI: The Ethics of AI Transparency” on SSRN. Here is the abstract:

Firms have obligations to stakeholders that do not disappear when managers adopt AI decision systems. We introduce the concept of the AI knowledge gap – where AI provides limited information about its operations while the stakeholder demands for information justifying firm decisions increase. We develop a framework of what firms must know about their AI model in the procurement process to ensure they understand how the model allows a firm to meet existing obligations including the anticipated risks of using the AI decision system, how to prevent foreseeable risks, and have a plan for resilience. We argue there are no conditions where it is ethical to unquestioningly adopt recommendations from a black box AI program within an organization. According to this argument, adequate comprehension and knowledge about an AI model is not a negotiable design feature but a strategic and moral requirement.