Suchergebnisse

Amazon Mechanical Turk (AMT) is an online labor market that defines itself as "a marketplace for work that requires human intelligence." Early advocates and developers of crowdsourcing platforms argued that crowdsourcing tasks are designed so people of any skill level can do this labor online. However, as the popularity of crowdsourcing work has grown, the crowdsourcing literature has identified a peculiar issue: that work quality of workers is not responsive to changes in price. This means that unlike what economic theory would predict, paying crowdworkers higher wages does not lead to higher quality work. This has led some to believe that platforms, like AMT, attract poor quality workers. This article examines different market dynamics that might, unwittingly, contribute to the inefficiencies in the market that generate poor work quality. We argue that the cultural logics and socioeconomic values embedded in AMT's platform design generate a greater amount of market power for requesters (those posting tasks) than for individuals doing tasks for pay (crowdworkers). We attribute the uneven distribution of market power among participants to labor market frictions, primarily characterized by uncompetitive wage posting and incomplete information. Finally, recommendations are made for how to tackle these frictions when contemplating the design of an online labor market.

Abstract
Generative artificial intelligence (AI) has the potential to both exacerbate and ameliorate existing socioeconomic inequalities. In this article, we provide a state-of-the-art interdisciplinary overview of the potential impacts of generative AI on (mis)information and three information-intensive domains: work, education, and healthcare. Our goal is to highlight how generative AI could worsen existing inequalities while illuminating how AI may help mitigate pervasive social problems. In the information domain, generative AI can democratize content creation and access but may dramatically expand the production and proliferation of misinformation. In the workplace, it can boost productivity and create new jobs, but the benefits will likely be distributed unevenly. In education, it offers personalized learning, but may widen the digital divide. In healthcare, it might improve diagnostics and accessibility, but could deepen pre-existing inequalities. In each section, we cover a specific topic, evaluate existing research, identify critical gaps, and recommend research directions, including explicit trade-offs that complicate the derivation of a priori hypotheses. We conclude with a section highlighting the role of policymaking to maximize generative AI's potential to reduce inequalities while mitigating its harmful effects. We discuss strengths and weaknesses of existing policy frameworks in the European Union, the United States, and the United Kingdom, observing that each fails to fully confront the socioeconomic challenges we have identified. We propose several concrete policies that could promote shared prosperity through the advancement of generative AI. This article emphasizes the need for interdisciplinary collaborations to understand and address the complex challenges of generative AI.