Reconceptualizing the Theory of the Firm-From Nature to Function

What is the firm ? This Article revisits and explores the theory of the firm and corporate personhood and shows how the century-old discourse in this area still firmly shapes how scholars, judges, and legislatures treat legal entities in corporate law, constitutional law, tort law, and criminal law, causing unnecessary complications and flawed outcomes. Traditionally, the firm is characterized as a real entity, a fiction, or an aggregate. Conversely, this Article proposes a novel answer to the perennial question as to how to conceptualize the firm. The new approach refocuses the debate away from the nature of the firm and contends that explanations of the firm should focus instead on its economic and social function, purpose, and effects. It also argues that compared to current approaches, a purely functional approach, as developed in greater detail in the Article, provides a more useful analytical framework to ascertain what rights and duties corporations and other legal entities should have

Lost Synergies and M&A Damages: Considering Cineplex v Cineworld

What is the appropriate remedy when an M&A transaction fails to close because of the acquirer’s breach of contract? Even before the controversy surrounding Elon Musk’s proposed acquisition of Twitter in the US, this question arose recently in Canada. In Cineplex v Cineworld, the Ontario Superior Court of Justice awarded $1.24 billion in damages based upon the target’s loss of anticipated synergies. This article highlights the problems with this approach, including conceptual and reliability issues with calculating and apportioning synergies to one entity in a business combination and significant variation in the availability and size of damages depending on transaction structuring and the financial or strategic nature of the buyer or deal. To avoid many of these issues and provide more consistent outcomes, we argue that courts should award specific performance, where feasible, or alternatively loss of consideration to shareholders as the seller’s or target’s damages. This latter measure best approximates the target corporation’s lost bargain and expectations and has the least reliability issues

Stuck in Neutral? Reforming Corporate Purpose and Fiduciary Duties

After decades of corporate leadership based on shareholder wealth maximization, momentum is now gathering behind a shift towards the recognition of stakeholder interests. However, from voluntary actions by business to changes in soft and hard law, the steps taken thus far have been insufficient to result in meaningful changes. Instead, we are stuck in neutral. A more decisive push is needed to ensure that business contributes to tackling the most pressing societal issues of our times in a substantial and timely manner. The Canadian corporate landscape, although beginning to shift away from shareholder primacy, is still not settled and in many ways has stagnated since the Supreme Court’s decisions in People’s and BCE. The CBCA’s new section 122(1.1) codifies that case law and therefore cannot be expected to provide a new impetus. Drawing from the experience in the United Kingdom, which previously introduced legislation similar to the Canadian reforms, we suggest that more than minor tweaks to corporate law are necessary to achieve meaningful and timely change. Working along with regulation in other areas of the law, corporate law can help transform corporate acts away from a solitary focus on shareholder wealth maximization if it offers mandatory and tailored mandates that guide corporations to prescribed outcomes. We propose therefore legislative changes to re-define corporate purpose more broadly and implement a mandatory system of balancing of shareholder and stakeholder interests by corporate leadership, with an emphasis on protection and advancement of human rights and environmental considerations

Corporate Management in the Age of AI

Recent media reports and press releases have created the impression that Artificial Intelligence (AI) is on the verge of assuming an important role in corporate management. While, upon closer inspection, it turns out that these stories should not always be taken at face value, they clearly highlight AI’s growing importance in management and hint at the enormous changes that corporate leadership may experience in the future. This Article attempts to anticipate that future by exploring a thought experiment on corporate management and AI. It argues that it is not an insurmountable step from AI generating and suggesting expert decisions (which is already common today) to AI making these decisions autonomously. The Article then proceeds based on the assumption that next-generation AI will be able to take over the management of business organizations and explores the corporate law and governance consequences of this development. In doing so, the Article focuses on the fundamental areas of corporate leadership and management structures, managerial liability, and the corporate purpose. It also considers the phenomenon of algorithmic entities and leaderless entities

Lateral tunnel epitaxy of GaAs in lithographically defined cavities on 220 nm silicon-on-insulator

Current heterogeneous Si photonics usually bond III–V wafers/dies on a silicon-on-insulator (SOI) substrate in a back-end process, whereas monolithic integration by direct epitaxy could benefit from a front-end process where III–V materials are grown prior to the fabrication of passive optical circuits. Here we demonstrate a front-end-of-line (FEOL) processing and epitaxy approach on Si photonics 220 nm (001) SOI wafers to enable positioning dislocation-free GaAs layers in lithographically defined cavities right on top of the buried oxide layer. Thanks to the defect confinement in lateral growth, threading dislocations generated from the III–V/Si interface are effectively trapped within ∼250 nm of the Si surface. This demonstrates the potential of in-plane co-integration of III–Vs with Si on mainstream 220 nm SOI platform without relying on thick, defective buffer layers. The challenges associated with planar defects and coalescence into larger membranes for the integration of on-chip optical devices are also discussed

On Assessing Trustworthy AI in Healthcare. Machine Learning as a Supportive Tool to Recognize Cardiac Arrest in Emergency Calls

Artificial Intelligence (AI) has the potential to greatly improve the delivery of healthcare and other services that advance population health and wellbeing. However, the use of AI in healthcare also brings potential risks that may cause unintended harm. To guide future developments in AI, the High-Level Expert Group on AI set up by the European Commission (EC), recently published ethics guidelines for what it terms “trustworthy” AI. These guidelines are aimed at a variety of stakeholders, especially guiding practitioners toward more ethical and more robust applications of AI. In line with efforts of the EC, AI ethics scholarship focuses increasingly on converting abstract principles into actionable recommendations. However, the interpretation, relevance, and implementation of trustworthy AI depend on the domain and the context in which the AI system is used. The main contribution of this paper is to demonstrate how to use the general AI HLEG trustworthy AI guidelines in practice in the healthcare domain. To this end, we present a best practice of assessing the use of machine learning as a supportive tool to recognize cardiac arrest in emergency calls. The AI system under assessment is currently in use in the city of Copenhagen in Denmark. The assessment is accomplished by an independent team composed of philosophers, policy makers, social scientists, technical, legal, and medical experts. By leveraging an interdisciplinary team, we aim to expose the complex trade-offs and the necessity for such thorough human review when tackling socio-technical applications of AI in healthcare. For the assessment, we use a process to assess trustworthy AI, called 1Z-Inspection® to identify specific challenges and potential ethical trade-offs when we consider AI in practice.</jats:p