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Register a new userConfronting Structural Inequities in AI for Education
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Michael Madaio
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Educational technologies, and the systems of schooling in which they are deployed, enact particular ideologies about what is important to know and how learners should learn. As artificial intelligence technologies -- in education and beyond -- have led to inequitable outcomes for marginalized communities, various approaches have been developed to evaluate and mitigate AI systems disparate impact. However, we argue in this paper that the dominant paradigm of evaluating fairness on the basis of performance disparities in AI models is inadequate for confronting the structural inequities that educational AI systems (re)produce. We draw on a lens of structural injustice informed by critical theory and Black feminist scholarship to critically interrogate several widely-studied and widely-adopted categories of educational AI systems and demonstrate how educational AI technologies are bound up in and reproduce historical legacies of structural injustice and inequity, regardless of the parity of their models performance. We close with alternative visions for a more equitable future for educational AI research.
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In the age of Artificial Intelligence and automation, machines have taken over many key managerial tasks. Replacing managers with AI systems may have a negative impact on workers outcomes. It is unclear if workers receive the same benefits from their relationships with AI systems, raising the question: What degree does the relationship between AI systems and workers impact worker outcomes? We draw on IT identity to understand the influence of identification with AI systems on job performance. From this theoretical perspective, we propose a research model and conduct a survey of 97 MTurk workers to test the model. The findings reveal that work role identity and organizational identity are key determinants of identification with AI systems. Furthermore, the findings show that identification with AI systems does increase job performance.
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Artificial intelligence shows promise for solving many practical societal problems in areas such as healthcare and transportation. However, the current mechanisms for AI model diffusion such as Github code repositories, academic project webpages, and commercial AI marketplaces have some limitations; for example, a lack of monetization methods, model traceability, and model auditabilty. In this work, we sketch guidelines for a new AI diffusion method based on a decentralized online marketplace. We consider the technical, economic, and regulatory aspects of such a marketplace including a discussion of solutions for problems in these areas. Finally, we include a comparative analysis of several current AI marketplaces that are already available or in development. We find that most of these marketplaces are centralized commercial marketplaces with relatively few models.
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