Algorithmic Opacity, Private Accountability, and Corporate Social Disclosure in the Age of Artificial intelligence
23(1) Vanderbilt Journal of Entertainment & Technology Law 99 (2021)
62 Pages Posted: 2 Jun 2020 Last revised: 8 Jan 2021
Date Written: December 4, 2020
Today, firms develop machine learning algorithms in nearly every industry to control human decisions, creating a structural tension between commercial opacity and democratic transparency. In many forms of business applications, advanced algorithms are technically complicated and privately owned, hiding from legal regimes and preventing public scrutiny, although they may demonstrate their erosion of democratic norms, damages to financial gains, and extending harms to stakeholders without warning. Nevertheless, because the inner workings and applications of algorithms are generally incomprehensible and protected as trade secrets, they can be completely shielded from public surveillance. One of the solutions to this conflict between algorithmic opacity and democratic transparency is an effective mechanism that incentivizes firms to engage in information disclosure for their algorithms.
This Article argues that the pressing problem of algorithmic opacity is due to the regulatory void of US disclosure regulations that fail to consider the informational needs of stakeholders in the age of AI. In a world of privately-owned algorithms, advanced algorithms as the primary source of decision-making power have produced various perils for the public and firms themselves, particularly in the context of the capital market. While the current disclosure framework has not considered the informational needs associated with algorithmic opacity, this Article argues that algorithmic disclosure under securities law could be used to promote private accountability and further public interest in sustainability.
First, as I discuss, advanced machine learning algorithms have been widely applied in AI systems in many critical industries, including financial services, medical services, and transportation services. Second, despite the growing pervasiveness of algorithms, the laws, particularly intellectual property laws, continue to encourage the existence of algorithmic opacity. Although the protection of trade secrecy in algorithms seems beneficial for firms to create competitive advantage, as I examine, it has proven deleterious for society, where democratic norms such as privacy, equality, and safety are now being compromised by invisible algorithms that no one can ever scrutinize. Third, although the emerging perils of algorithmic opacity are much more catastrophic and messier than before, the current disclosure framework in the context of corporate securities laws fails to consider the informational needs of the stakeholders for advanced algorithms in AI systems.
In this vein, through the lens of the US Securities and Exchange Commission (SEC) disclosure framework, this Article proposes a new disclosure framework for machine-learning-algorithm-based AI systems that considers the technical traits of advanced algorithms, potential dangers of AI systems, and regulatory governance systems in light of increasing AI incidents. Towards this goal, I discuss numerous disclosure topics, analyze key disclosure reports, and propose new principles to help reduce algorithmic opacity, including stakeholder consideration, sustainability consideration, comprehensible disclosure, and minimum necessary disclosure, which I argue can ultimately strike a balance between democratic values in transparency and private interests in opacity. This Article concludes with a discussion of the impacts, limitations, and possibilities of using the new disclosure framework to promote private accountability and corporate social responsibility in the AI era.
Keywords: Artificial Intelligence, Algorithmic Opacity, Accountability, Disclosure, Trade Secret, Sustainability, Non-financial reporting, Disclosure Framework, CSR, Corporate Social Responsibility, Corporate Governance, Securities Law & Regulation, Algorithmic Transparency, AI, Black Box, Algorithm
JEL Classification: K22, M14, C80, K00, K19, G38, G39, G00
Suggested Citation: Suggested Citation