TPRC 2024: The 52nd Research Conference on Communications, Information and Inter

Link to Paper

Abstract:
The rapid evolution of today’s technology landscape, driven by algorithm-driven systems (ADS), profoundly impacts everyday life and major societal functions. ADS, often perceived as mysterious or magical, are sophisticated computational methods that enable automated decision-making in various domains such as finance, employment, and social media content delivery. Despite their pervasive influence, understanding of these systems remains limited among the general public, posing risks to informed citizenship and democratic participation. This paper addresses the critical need for Algorithmic Literacy, a concept that extends beyond existing frameworks of media and digital literacy to encompass a basic proficiency in understanding how ADS function and influence personal and societal outcomes. Our research identifies a significant gap in Algorithmic Literacy among social media users, exacerbated by the increasing complexity and opacity of AI technologies. We propose a preliminary definition of Algorithmic Literacy and argue for its necessity in contemporary society. The paper outlines initial measures for fostering Algorithmic Literacy and sets a future research agenda to support these efforts. Our goal is to initiate a critical discourse on Algorithmic Literacy, calling on citizens, educators, policymakers, media professionals, and technologists to prioritize this issue to ensure informed and active participation in an increasingly algorithm-driven world.

Link to paper

Abstract:
This study examines the posited relationship between diversity and the supply of debiased AI, using a cross-sectional survey sample of AI professionals working in Silicon Valley. The results of preliminary analyses show that increased diversity, when indicated by value diversity as opposed to demographic diversity, had a significant effect on decreased AI bias. Further, the analysis found that there was no significant difference in manufactured AI bias or the effort to debias AI attributable to the socio-demographic diversity alone, as self-reported by AI industry insiders. The study concludes with a call for much closer attention 1) to diversity in its conceptual and regulatory operationalization and 2) to conditional institutional variables in translating diversity into discernible effects. The author of this study emphasizes a preliminary nature of the findings, with suggestions for the potential areas of improvement in the future AI debate.

Link to paper

Abstract:
This paper is a comparative analysis of how two leading developing nations, China and India, are proposing to regulate artificial intelligence (AI) systems. Despite similarity in circumstances as large developing economies aiming to upgrade their technology sectors and create jobs, the two countries have taken significantly different approaches to AI regulation. We discuss the reasons why, based on review of agency reports and documents in the two countries.