This module is designed to enhance students' understanding of AI regulation through the use of case studies as a methodological tool. By examining real-world scenarios, students will critically assess the necessity for regulation and the associated risks and challenges within data-driven environments. This approach not only facilitates the practical application of concepts and analytical frameworks essential for ensuring algorithmic fairness but also encourages students to extract meaningful insights while navigating complex interactions among various actors mediated by AI. In so doing, students will acquire a more concrete understanding of the problems, conflicts, and dilemmas associated with: (1) deciding whether or not a data-driven technology should be regulated and if so, how; (2) identifying and defining the appropriate overarching goals that regulation should seek to achieve, along with the specific set of regulatory norms necessary to meet those objectives; (3) the range of actors, institutions, instruments, and techniques that are or could be involved in technology governance, including establishing an initial regulatory framework and institutional architecture, and the roles they might play; (4) the dynamic, complex, and often supra-national dimensions of technological development, acceptance, and diffusion; and (5) developing, implementing, and critically evaluating whether and to what extent regulatory frameworks, institutions, and practices can be regarded as effective and legitimate. Through this module, students will cultivate a nuanced understanding of the multifaceted challenges involved in regulating emerging technologies like AI.
Learning Outcomes
By the end of the module students should be able to:
Reflect critically on the 'stakes' – legal, ethical, moral, political, social and economic – arising within recent and on-going debates about 'technological risk' in contemporary cases concerned with the impact of AI on society, including the nature of the perceived risks, the level at which emerging regulatory responses are no longer confined to a single jurisdiction, and the interplay of multiple and varied actors, organisations and institutions (often in co-operation with state institutions) in shaping data governance;
Demonstrate understanding of through case study investigation, paired with an ability to identify and assess nature and magnitude of possible threats, harms and 'risks' associated with process of scientific research and technological innovation and contested over whether such risks arise in relation to data driven technologies;
Reflect critically, through case study investigation, on the range of regulatory institutions, practices and processes concerned with managing scientific, technological and related risks relate to various theoretical understandings into the nature, types and magnitude of technological threats and risks as well as the political, economic and moral questions raised by the emergence of AI technologies in particular social contexts;
Explore, through case study investigation (a) select data driven systems and contexts in which threats and risks associated with their emergence attract public and academic concerns about their potential implications and acceptability, and (b) the importance of social acceptability of AI technologies, including the role of human rights as \"moral boundary objects\".
