REGULATING ALGORITHMIC ASSEMBLAGES: EXPLORING BEYOND CORPORATE AI ETHICS

Abstract

The rapid advancement of artificial intelligence (AI) systems, fueled by extensive research and development investments, has ushered in a new era where AI permeates decision-making processes across various sectors. This proliferation is largely attributed to the availability of vast digital datasets, particularly in machine learning, enabling AI systems to discern intricate correlations and furnish valuable insights from data on human behavior and other phenomena. However, the widespread integration of AI into private and public domains has raised concerns regarding the neutrality and objectivity of automated decision-making processes. Such systems, despite their technological sophistication, are not immune to biases and ethical dilemmas inherent in human judgments. Consequently, there is a growing call for regulatory oversight to ensure transparency and accountability in AI deployment, akin to traditional regulatory frameworks governing analogous processes. This paper critically examines the implications and ripple effects of incorporating AI into existing social systems from an 'AI ethics' standpoint. It questions the adequacy of self-policing mechanisms advocated by corporate entities, highlighting inherent limitations in corporate social responsibility paradigms. Additionally, it scrutinizes well-intentioned regulatory initiatives, such as the EU AI ethics initiative, which may overlook broader societal impacts while prioritizing the desirability of AI applications. The discussion underscores the necessity of adopting a holistic approach that transcends individual and group rights considerations to address the profound societal implications of AI, encapsulated by the concept of 'algorithmic assemblage'