AFFILIATE RESEARCH

By Myojung Chung and John Wihbey | August 2024

While understanding how social media algorithms operate is essential to protect oneself from misinformation, such understanding is often unevenly distributed. This study explores the algorithmic knowledge gap both within and between countries, using national surveys in the United States (N = 1,415), the United Kingdom (N = 1,435), South Korea (N = 1,798), and Mexico (N = 784). In all countries, algorithmic knowledge varied across different sociodemographic factors, even though in different ways. Also, different countries had different levels of algorithmic knowledge: The respondents in the United States reported the greatest algorithmic knowledge, followed by respondents in the United Kingdom, Mexico, and South Korea. Additionally, individuals with greater algorithmic knowledge were more inclined to take actions against misinformation. Learn More >>

Other Affiliate Research

A Case Study in an A.I.-Assisted Content Audit

A Case Study in an A.I.-Assisted Content Audit

This paper presents an experimental case study utilizing machine learning and generative AI to audit content diversity in a hyper- local news outlet, The Scope, based at a university and focused on underrepresented communities in Boston. Through computational text analysis, including entity extraction, topic labeling, and quote extraction and attribution, we evaluate the extent to which The Scope’s coverage aligns with its mission to amplify diverse voices.

AI Regulation: Competition, Arbitrage & Regulatory Capture

AI Regulation: Competition, Arbitrage & Regulatory Capture

The commercial launch of ChatGPT in November 2022 and the fast development of Large Language Models catapulted the regulation of Artificial Intelligence to the forefront of policy debates One overlooked area is the political economy of these regulatory initiatives–or how countries and companies can behave strategically and use different regulatory levers to protect their interests in the international competition on how to regulate AI.
This Article helps fill this gap by shedding light on the tradeoffs involved in the design of AI regulatory regimes in a world where: (i) governments compete with other governments to use AI regulation, privacy, and intellectual property regimes to promote their national interests; and (ii) companies behave strategically in this competition, sometimes trying to capture the regulatory framework.

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Are you interested in joining the IDI team or have a story to tell? reach out to us at j.wihbey@northeastern.edu