AFFILIATE RESEARCH

By David Lazer | August 2024

American scientists are notably unrepresentative of the population. The disproportionately small number of scientists who are women, Black, Hispanic or Latino, from rural areas, religious, and from lower socioeconomic backgrounds has consequences. Speci cally, it means that, relative to their counterparts, individuals who identify as such are more dissimilar and more socially distant from scientists. These individuals, in turn, have less trust in scientists, which has palpable implications for health decisions and, potentially, mortality. Increasing the presence of underrepresented groups among scientists can increase trust, highlighting a vital bene t of diversifying science. This means expanding representation across several divides—not just gender and race but also rurality and socioeconomic circumstances. 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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